Method of identifying high immune response animals

ABSTRACT

The invention relates to a method and use of a method of identifying high immune response animals under stress. The animals are identified by a ranking procedure that classifies the animal&#39;s immune response to an antigen over a period of time that spans the stress.

[0001] This application is a Continuation-In-Part of U.S. Ser. No.09/215,328 filed on Dec. 18, 1998 (now allowed) which claims the benefitof U.S. Ser. No. 60/068,750 filed Dec. 24, 1997 (now abandoned).

FIELD OF THE INVENTION

[0002] The invention relates to a method of identifying and breedinghigh immune response animals within a population of animals understress, such as during peripartum.

BACKGROUND OF THE INVENTION

[0003] It has been found that there is an association between stress anddisease occurrence in animals (T. Molitor and L. Schwandtdt, “Role OfStress On Mediating Disease In Animals”, Proc. Stress Symposia:Mechanisms, Responses, Management. Ed., N. H. Granholm, South DakotaState University Press, Apr. 6-7, 1993). Further it has been suggestedthat stress can lead to a compromised immune system. (T. Molitor and L.Schwandtdt, “Role Of Stress On Mediating Disease In Animals”, Proc.Stress Symposia: Mechanisms, Responses, Management. Ed., N. H. Granholm,South Dakaota State University Press, Apr. 6-7, 1993/ Morrow-Tesch J. L.et al. 1996 J. Therm. Biol. 21(2):101-108) This can have significanteffect on populations of animals such as commercial livestock includingcattle, pigs, poultry, horses, and fish, wherein stress can be relatedto growth inhibition, infertility, and decreased milk or egg production(where applicable). It has been shown that the peripartum period orperiparturition, in animals is a period of stress. (L. G. Johnson,“Temperature Tolerance, Temperature Stress, and Animal Development”,Proc. Stress Symposia: Mechanisms, Responses, Management. Ed., N. H.Granholm, South Dakaota State University Press, Apr. 6-7, 1993; J. J.McGloner, “Indicators Of Stress In Livestock And Implications ForAdvancements In Livestock Housing”, Proc. Stress Symposia, : Mechanisms,Responses, Management. Ed., N. H. Granholm, South Dakaota StateUniversity Press, Apr. 6-7, 1993; T. Molitor and L. Schwandtdt, “Role OfStress On Mediating Disease In Animals”, Proc. Stress Symposia:Mechanisms, Responses, Management. Ed., N. H. Granholm, South DakaotaState University Press, Apr. 6-7, 1993; M. J. C. Hessing et al, “SocialRank And Disease Susceptibility In Pigs”, Vet Immunol. Immunopath43:373-387, 1994; F. Blecha, “Immunoligcal Reactions Of Pigs RegroupedAt Or Near Weaning”, Am. J. Vet. Res. 46(9): 1934-1937, 1985; D. L.Thompson et al., “Cell Mediated Immunity In Marek's DiseaseVirus-Infected Chickens Genetically Selected For High and LowConcentrations Of Plasma Corticosterone”, Am. J. Vet. Res. 41(1):91-96,1980; Kehrli, H. E. et al., 1989a & b, Am. J. Vet. Res. 50(2):207 and215).

[0004] Impairment of bovine host defense during the peripartum periodmay be associated with high concurrent disease occurrence. Impairedresistance may be due to endocrine factors associated with metabolic andphysical changes occurring during gestation, parturition and lactation(Smith et al., 1973; Guidry et al., 1976; Burton et al., 1993).Infectious diseases of the peripartum period include mastitis, metritisand pneumonia. Metabolic and some reproductive diseases also predominateduring this period and include retained placenta, milk fever, ketosis,and displaced abomasum. Mastitis is the most economically relevantdisease. Estimated annual losses from mastitis are $35 billion (U.S)worldwide (Giraudo et al. 1997), $2 billion (U.S.) in the United States(Harmon, 1994) and $ 17 million (Can.) in Canada ($140-300 Can./cow)(Zhang et al., 1993).

[0005] Mastitis is an inflammation of the mammary gland characterized bylocal and systemic responses (Burvenich et al., 1994). Mastitis can beclinical or subclinical, when signs are not directly observable, butsomatic cell counts in milk (SCC) increase and overall productionperformance decreases. Mastitis is caused by a number of Gram positiveand Gram negative bacteria which are either major or minor pathogens.Major pathogens induce the greatest compositional changes in milk andhave the greatest economic impact (Harmon, 1994). They includeStaphylococcus aureus, Escherichia coli, Streptococcus agalactiae,Klebsiella spp., and others, while minor pathogens include coagulasenegative staphylococci, and Corynebacterium bovis. The incidence ofudder infection and clinical mastitis is usually highest at parturitionand during early lactation (Smith et al., 1985). Coliforms such as E.coli and Klebsiella are the most common major pathogen during thisperiod. Since coliform mastitis is difficult to treat, natural defencemechanisms of the mammary gland have been investigated in pursuit ofcontrol procedures (Burvenich et al., 1994). Coliform mastitis may beperacute and fatal, or subclinical. Most commonly it is acute clinicalmastitis, with local and systemic signs of disease. Coliforms areGram-negative microorganisms from the family Enterobacteriaceae whichinclude important species from the genera Escherichia, Klebsiella,Enterobacter, Citrobacter and Proteus (Harmon, 1994; Kremer et al.,1994). The structure of the cell wall of coliform bacteria plays animportant role in the virulence of the bacteria and subsequently in thepathogenesis of mastitis. The cell wall of E. coli has an innercytoplasmic membrane, a peptidoglycan layer, an outer membrane thatconsists of two layers: a phospholipid protein layer and an outerlipopolysaccharide layer (LPS), and finally some strains possess anadditional capsular polysaccharide layer. The LPS layer has threecomponents: the O-specific polysaccharide chain, a polysaccharide core,and lipid A. Lipid A mediates the biological properties of LPS(endotoxin). Endotoxemia causes clinical signs of disease including highfever, drowsiness, appetite loss, dehydration, loss in milk production,cardiovascular failure, shock and often death (Kremer et al., 1994;Burvenich et al., 1994). Factors which contribute to susceptibility tomastitis include the complex environment (pasture, bedding, cleanlinessof holding areas), management (milking practices, antibiotic therapyduring lactation and dry-off) and physical trauma to the teat and/orudder (Cullor, 1995).

[0006] Various attempts have been made to develop vaccines against S.aureus as a treatment for mastitis, but without success. Vaccines haveincluded toxoid, protein A, capsule and fibronectin in varyingcombinations and concentrations (reviewed by Sordillo, 1995). Whilethese preparations may reduce the severity and duration of mastitis, newinfections are not prevented. Inclusion of capsular polysaccharide invaccine preparation slightly reduced the rate of new infection (Watsonand Schwartskoff, 1990). More recently, the combination of a crudeextract of S. aureus exopolysaccharides and inactivated unencapsulatedS. aureus and Streptococcus spp. in a vaccine decreased incidence ofintramammary infections caused by S. aureus (Giraudo et al., 1997).Newer vaccines against environmental coliforms contain rough orR-mutants of E. coli or Salmonella typhimurium. The surface coreantigens of these mutants induces formation of cross-protective antibodythat provides protection against various gram-negative diseases ofanimals including mastitis and calf scours. (Parker et al., 1994). Thesevaccines decrease incidence and severity of clinical disease but do notaffect prevalence of coliform infections (Sordillo, 1995).

[0007] Direct selection for disease resistance may be done either byselecting the most disease-resistant breeding stock under normalenvironmental conditions, or by challenging the breeding stock withspecific pathogens (Hutt, 1959). Indirect selection is based onidentification of reliable indirect markers of disease resistance(Detilleux et al., 1993). Phenotypic indicators include morphologicalmarkers (eg. eye margin pigmentation in bovine infectiouskeraconjunctivitis), physiological markers (eg. hemoglobin type inmalaria), and innate or immune response traits (eg. PMN function,antibody response and CMI). Genotypic indicators include candidate genes(eg. MHC genes, Ig genes, TcR genes), and anonymous molecular geneticmarkers (eg. RFLPs, tandem repeats loci, microsatellite loci) (Detilleuxet al., 1993).

[0008] Experiments using immune response variation as selection criteriahave been successful at directing response to be high or low (Biozzi etal., 1968; Ibanez et al., 1980; Siegel et al., 1980; Van der Zijpp etal., 1983; and Mallard et al., 1992). The continuous distributionantibody response suggests that response is under multigenic control(Puel and Mouton, 1996) and that characteristic quantitative antibodyresponsiveness is controlled by several independently segregating loci(Stiffel et al., 1987). The first selection experiment using antibodyresponse following immunization was reported in guinea pigsassortatively mated for five generations. The immunogen used wasdiphtheria anatoxin and the immune responses of progeny wereprogressively modified in upward and downward directions (Shiebel,1943). A similar experiment was conducted using rabbits selected for twogenerations based on antibody produced to Streptococcus sp. (Eichmann etal., 1971). A more extensive examination of antibody responsevariability in mice was demonstrated by Biozzi et al. (1979). Severalindependent selective matings were carried out with mice for antibodyresponsiveness to sheep red blood cells (SRBCs). SRBCs aremultideterminant antigens which are strongly immunogenic in all strainsof mice (Puel and Mouton, 1996). Assortative mating of mice with extremephenotypes in upward or downward directions were repeated for successivegenerations until maximal divergence of the two lines was achieved(Biozzi et al., 1972). The relevance of this dichotomy pertains to theability of mice to mount strong responses, either antibody or cellmediated immune response, to extra or intra cellular organisms. The lowline (L line) was determined to be more resistant than the high line (Hline) to intra-cellular organisms such as Salmonellae, Yersinia,Mycobacteria, and Brucellae, and when the macrophage provides thedominant defensive barrier. The H line was more resistant toextracellular microorganism including Pneumococcus, Klebsiella,Plasmodia, and Trypanosoma. The major genetic modification whichexplained differences between these selected lines was at the level ofthe macrophage. Antigen was observed to be slowly catabolized andpersisted on the macrophage membrane of the H line mice, whereas it wasrapidly destroyed in L line macrophages. Selection of chickens based onantibody response to SRBC has also demonstrated variation and theconsequent divergence of high and low lines of chickens (Siegel andGross, 1980; Van der Zijpp et al., 1983; Pinard et al., 1992). Antibodyresponse to SRBC and chicken erythrocytes was similarly evaluated inguinea pigs, which diverged to high and low immune response lines aftersuccessive selection for 8 generations (Ibanez et al., 1980). Yorkshirepigs selected using estimated breeding values (EBVs) for both antibodyand cell mediated immune response, were reported to diverge into highand low immune response lines (Mallard et al., 1992). The maximumdivergence of high and low responses were observed between generation 1(G₁) and 3 (G₃) with little or no response to selection after generation4 (G₄) (Mallard et al., 1997). Although a few studies have examined theeffect that selecting for milk production has on various innate andimmune response parameters, no breeding studies have been conductedusing immune response variation as selection criteria.

[0009] Selective breeding of cattle for resistance to mastitis usingsomatic cell count (SCC) is currently under evaluation. Current industrytrends favour a low somatic cell count in milk secretions. A SCC that istoo low may be detrimental to innate mechanisms of resistance tomastitis and therefore must be used with caution. Genetic correlationbetween SCC and mastitis vary, but values are mainly positive (r=0.81;Madsen, 1989; r=0.3, Weller et al., 1996). SCC is now considered theprimary trait used to evaluate susceptibility to mastitis which enablesindirect selection for resistance to mastitis (Shook, 1994; Dekkers etal., 1998). Selection based on occurrence of clinical mastitis isunreliable since it is not routinely recorded, it has complex aetiology,and observations on the occurrence and severity of mastitis aresubjectively evaluated by producers. Several records on SCC areavailable through dairy herd improvement corporations which provide asubstantial database from which to determine estimated breeding valuesfor SCC. SCC and its logarithmic transformation, SCS, have higherheritability (h²=ranging between 0.10-0.12) (Emmanuelson et al., 1988;Banos and Shook, 1990; Boettcher et al., 1992) than clinical mastitis(h²=0.03) (Emmanuelson, 1988; Madsen, 1989). However, low heritabilityestimates of SCS, in contrast to some production traits, indicate thatSCS is not influenced to a greater degree by environmental factors. Lowheritabilities suggest that SCS and mastitis will respond more slowly togenetic improvement than milk yield (Shook, 1993; Boettcher et al.,1992). Research conducted in Ontario by Dekkers and Burnside (1994)evaluating estimated transmitting abilities (ETAs) for linear somaticcell score (LSCS) indicated that daughters of the poorest sires haddouble the average SCC (transformed from LSCS) of daughters of the bestsires, and, sires whose daughters had a higher LSCS tend to have moremastitis problems. This research indicated that, although adding LSCS togenetic selection will reduce genetic progress for production by <2percent, it will also slow down the current genetic deterioration ofresistance to mastitis. Its inclusion would be relevant since therewould be lower treatment and other related mastitis costs and therewould be an increase in the revenue per cow per year by 0.3 to 1.0percent, despite a slight decrease in milk sales. While there is somebenefit to using SCS as a selection tool, it is not as heritable as someaspects of immune response phenotype. Antibody response to ovalbumin(OVA) in dairy calves was reported by Burton et al. (1989) to bemoderately heritable (h²=0.48), and in contrast to SCS may be morepromising as a selection tool for improved inherent disease resistance(Burton et al., 1989).

[0010] Dekkers et al. (1996a) recently developed a sire index called thetotal economic value index (TEV) which includes economically weightedtraits of importance. It includes production, herd life and udderhealth. Production accounts for 64% of the TEV, herd life for 26% andudder health, which includes SCS, accounts for 10% of the TEV. Whileproduction still is the most economically important, more emphasis cannow be placed on the costs associated with mastitis by evaluating SCS.Once more heritable candidate markers of immune response are determined,more information about udder health could be added to the TEV.

SUMMARY OF THE INVENTION

[0011] The present invention relates to a method of identifying highimmune response animals under stress and a method of determining ananimal's susceptibility to stress related disease. The method involvesevaluating the change in the animal's antibody response to an antigenover time intervals spanning the stress, for example in periparturition,the pre- and postpartum period. Based on the changes in the response tothe antigen, the animals can be classified as a high, average or lowimmune responder. Accordingly the present invention provides a method ofranking the immune response of a test animal within a population ofanimals under stress comprising:

[0012] (a) immunizing the animals with at least one antigen at leastonce before the onset of the stress; and

[0013] (b) measuring the antibody response of the animals to the atleast one antigen at least once before the onset of the stress and atleast once during the stress,

[0014] wherein a change in antibody response from before the onset ofstress to during the stress for the test animal that is greater than theaverage change in antibody response from before the onset of the stressto during the stress for the population indicates that the animal is ahigh immune responder.

[0015] According to another embodiment of the present invention there isprovided a method of ranking the immune response of a test animal withina population of animals under stress comprising:

[0016] (a) immunizing the animals with at least one antigen at leastonce before the onset of the stress and at least once during the stress;and

[0017] (b) measuring the antibody response of the animals to the atleast one antigen at least once before the onset of the stress and atleast once during the stress,

[0018] wherein a change in antibody response from before the onset ofstress to during the stress for the test animal that is greater than theaverage change in antibody response from before the onset of the stressto during the stress for the population indicates that the animal is ahigh immune responder. In preferred embodiments, the antibody responseof the animals to the at least one antigen is measured at least oncebefore the onset of the stress and at least twice during the stress andthe changes in antibody responses between each measurement are added toprovide a total antibody response and a total antibody response for thetest animal that is greater than an average total antibody response forthe population indicates that the animal is a high immune responder.

[0019] Where the stress is periparturition, the high immune responderscomprise animals that have a sustained antibody response (i.e. little orno decrease in antibody response) in both the pre and postpartum period.These animals are least likely to develop peripartum disease.

[0020] Measuring the change in antibody responses to the antigen overtime intervals, rather than at a discreet points in time, allowed thepresent inventors to develop a mathematical index which can be used torank the animals. The mathematical index as part of the immunization andmeasurement schedules of the present invention provide a method ofranking the immune response of a test animal within a population ofanimals under stress. The method with the index comprise the following:

[0021] (a) immunizing the animals with at least one antigen at leastonce before the onset of the stress and at least once during the stress;

[0022] (b) measuring the antibody response of the animals to the atleast one antigen at least once before the onset of the stress and atleast once during the stress; and

[0023] (c) calculating a mathematical index of the antibody response,wherein the mathematical index is: y=primary antibody response+secondaryantibody response+tertiary antibody response+quaternary antibodyresponse, wherein

[0024] (i) y is the immune response;

[0025] (ii) the primary response is the difference in antibody quantityat a first time point before the onset of stress and a second time pointduring the stress, wherein the animal is immunized at the first timepoint before the onset of stress;

[0026] (iii) the secondary response is the difference in antibodyquantity at a second time point during the stress and at a third timepoint during the stress, wherein the animal is immunized at the secondtime point during the stress;

[0027] (iv) the tertiary response is the difference in antibody quantityat a third time point during the stress and at a fourth time pointduring the stress, wherein the animal is immunized at the third timepoint during the stress; and

[0028] (v) the quaternary response is the difference in antibodyquantity at a fourth time point during the stress and at a fifth timepoint after the stress;

[0029] wherein with animals exhibiting negative secondary and/ortertiary antibody responses the secondary and/or tertiary antibodyresponses are weighted with a co-efficient greater than 1, and a testanimal having a y value greater than about one standard deviation abovethe average of the y value for the population is a high immuneresponder. The immune response, y, may, in other embodiments of theinvention, be calculated using the primary response only, the primaryplus secondary responses and/or the primary plus secondary plus tertiaryresponses.

[0030] The inventors have also shown that exposing a population ofanimals to an antigen which can evoke a cell mediated immune response(CMIR) and measuring at least one indicator of the CMIR of each animalduring stress, when combined with the immunization and measurement ofantibody schedule of the present invention, there is provided yetanother embodiment of the present invention for ranking the immuneresponse of a test animal within a population of animals under stress.According to this embodiment of the invention the method comprises:

[0031] (a) immunizing the animals with at least one antigen at leastonce before the onset of the stress;

[0032] (b) measuring the antibody response of the animals to the atleast one antigen at least once before the onset of the stress and atleast once during the stress;

[0033] (c) exposing the animals to an antigen which can evoke acell-mediated immune response (CMIR); and

[0034] (d) measuring at least one indicator of the CMIR in the animalsduring the stress,

[0035] wherein the changes in antibody responses between eachmeasurement are added to provide a total antibody response and themeasurement of the indicator is combined with the total antibodyresponse to provide an immune response and a test animal having animmune response that is greater than an average immune response for thepopulation indicates that the animal is a high immune responder.

[0036] The mathematical index as part of the immunization andmeasurement schedules of the present invention according to theembodiment just described provides a further embodiment of a method ofranking the immune response of a test animal within a population ofanimals under the stress of, for example, periparturition. The methodwith the index comprise the following:

[0037] (a) immunizing the animals with at least one antigen at leastonce before the onset of the stress and at least once during the stress;

[0038] (b) measuring the antibody response of the animals to the atleast one antigen at least once before the onset of the stress and atleast once during the stress;

[0039] (c) exposing the animals to an antigen which can evoke acell-mediated immune response (CMIR);

[0040] (d) measuring at least one indicator of the CMIR in the animalsduring the stress; and

[0041] (e) calculating a mathematical index of the antibody response andCMIR, wherein the mathematical index is: y=primary antibodyresponse+secondary antibody response+tertiary antibodyresponse+quaternary antibody response+CMIR, wherein

[0042] (i) y is the immune response;

[0043] (ii) the primary response is the difference in antibody quantityat a first time point before the onset of stress and a second time pointduring the stress, wherein the animal is immunized at the first timepoint before the onset of stress;

[0044] (iii) the secondary response is the difference in antibodyquantity at a second time point during the stress and at a third timepoint during the stress, wherein the animal is immunized at the secondtime point during the stress;

[0045] (iv) the tertiary response is the difference in antibody quantityat a thirdtime point during the stress and at a fourth time point duringthe stress, wherein the animal is immunized at the third time pointduring the stress;

[0046] (v) the quaternary response is the difference in antibodyquantity at a fourth time point during the stress and at a fifth timepoint after the stress; and

[0047] (vi) CMIR is the measurement obtained from at least one method ofdetermining CMIR,

[0048] wherein with animals exhibiting negative secondary and/ortertiary antibody responses, the secondary and/or tertiary antibodyresponses are weighted with a co-efficient greater than 1, and a testanimal having a y value greater than about one standard deviation abovethe average of the y value for the population is a high immuneresponder. The immune response, y, may, in other embodiments of theinvention, be calculated by combining CMIR with the primary responseonly, the primary plus secondary responses and/or the primary plussecondary plus tertiary responses.

[0049] The methods of ranking the animals according to the presentinvention can be used, for example, to identify animals that are leastsusceptible to developing a postpartum disease. In particular, thepresent inventors have demonstrated that high immune responder dairycows have a lower incidence of mastitis as compared to animals that areranked as average or low immune responders. Accordingly, the presentinvention provides a use of a method of the invention to identifyanimals that are selected from the group consisting of: animals that areless susceptible to developing a peripartum disease wherein antibodyquantity and quality are relevant host resistance factors; animals thatare less susceptible to developing a peripartum disease wherein antibodyquantity and quality and CMIR mediate broad-based disease resistance;animals with increased growth hormone; and animals with increased IGF-1outside the peripartum period and with decreased IGF-1 inside theperipartum period.

[0050] Once animals have been ranked by the method of the presentinvention, the high immune responder animals may be selectively breededin order to produce animals that have, for example, lower incidence ofperipartum disease. Further, the methods of the invention may also beused to identify low immune responders, those animals having immuneresponses, as determined using a method of the invention, that are lowerthan that of the average of the population. Such animals may be useful,for example, in drug screening/efficacy trials.

[0051] The methods of the present invention may be used in a wide rangeof animals including cows, pigs, chickens and other commercially usefulanimals.

[0052] Other features and advantages of the present invention willbecome apparent from the following detailed description. It should beunderstood, however, that the detailed description and the specificexamples while indicating preferred embodiments of the invention aregiven by way of illustration only, since various changes andmodifications within the spirit and scope of the invention will becomeapparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The invention will now be described in relation to the drawingsin which:

[0054]FIGS. 1A and B are graphs showing the anti-OVA antibody levelsversus time for animals of Group 1, Group 2 and Group 3.

[0055]FIG. 2 is a bar graph showing the percentage of disease occurrencein the animals of Group 1, Group 2 and Group 3.

[0056]FIG. 3 is a graph showing the anti-OVA antibody levels versus timefor the animals of Group 1, Group 2 and Group 3.

[0057] FIGS. 4A-C are graphs showing the anti-OVA antibody levels inwhey versus time for the animals in Group 1, Group 2 and Group 3.

[0058] FIGS. 5A-C is a graph showing the anti-E. coli antibody levelsversus time for the animals of Group 1, Group 2 and Group 3.

[0059]FIGS. 6A and B are bar graphs showing antibody levels versus timein the animals of Group 1, Group 2 and Group 3.

[0060]FIG. 7 is a bar graph showing the rate of mastitis occurrencebased on antibody response within a herd.

[0061]FIG. 8A-C is a graph showing the somatic cell score versus timefor the animals in Herd 1, Herd 2 and Herd 3.

[0062]FIG. 9 is a graph showing Con A stimulated lymphocyteproliferatives versus time for the animals of Group 1, Group 2 and Group3.

[0063]FIG. 10 is a bar graph showing the percent increase in skinthickness after challenge with PPD in cows and heifers.

[0064]FIG. 11 is a bar graph showing the lymphocyte counts versus timefor the animals in Group 1, Group 2 and Group 3.

[0065] FIGS. 12A-C are bar graphs showing the production versus antibodyresponse for the animals of Group 1, Group 2 and Group 3.

[0066]FIGS. 13A and B are graphs showing the anti-OVA antibody levelsversus time for the animals of Group 1, Group 2 and Group 3.

[0067] FIGS. 14A-C are graphs showing the hormone concentration versustime for the animals of Group 1, Group 2 and Group 3.

[0068]FIG. 15 is a bar graph showing the percentage disease occurrenceand the antibody response in the animals of Group 1, Group 2 and Group3.

DETAILED DESCRIPTION OF THE INVENTION

[0069] Definitions

[0070] “Adjuvant” as used herein, refers to any Adjuvant formulationsknown to stimulate an antibody response and/or CMIR. Examples include,but are not limited to, Freunds complete adjuvant (FCA), non-ulcerativeFreunds adjuvant (NUFA), complete NUFA (NUFA plus mycobacteria cell wallextract) and mycobacteria cell wall extract.

[0071] “Animal” as used herein includes all members of the animalkingdom. The methods of the present invention may be applied to a widevariety of species. Preferably, they are applied to commerciallyimportant animal species including: swine; cattle; sheep; avian species,such as chickens, and fish; horses; dogs; and cats.

[0072] “Antigen” as used herein, refers to any agent to which an animalis exposed and elicits the specified immune response. Suitable antigensfor use in the present invention can be of animal, bacterial, viral,synthetic, or other origin. or instance in cows, suitable antigensinclude but are not limited to ovalbumin, hen egg white lysozyme, humanseralbumin, red blood cells from any animal other than the cow;tyrosine-glutamine-alanine-lysine co-polymer (a synthetic antigen). Inchoosing suitable antigens for the present invention, the antigens arepreferably ones to which the animal is not normally exposed, andpreferably one to which they have not been exposed. A person skilled inthe art would appreciate that the preferred antigens will depend on theanimal species used. Preferably the antigen is either formulated with anAdjuvant or is formulated in to a vaccine.

[0073] “Disease resistance or susceptibility” refers to resistance orsusceptibility to clinical or subclinical conditions of severalpotential aetiologies including infectious, neoplastic, orstress-related. Examples of diseases resulting from infectious agentsinclude but are not limited to peritonitis, pleuritis, pericarditis,mastitis, dermititis, enteritis, pneumonia, encephalitis, myelitis, andmetritis. The term “disease resistance or susceptibility” herein alsorefers to responsiveness to vaccination and to therapy such asantibiotics.

[0074] “Estimated Breeding Value” or “EBV” as used herein, refers to adetermined numeric value of a phenotypic trait which takes into accountmeasurements of the trait in the individual and its relatives, therebypredicting the genetic ability of the individual to transmit the traitto its offspring.

[0075] The term “greater than average antibody response” as used hereinmeans the change in the production of antibody in response to an antigenbetween specifed periods of time, that change, or additive changes, inan amount being greater than approximately one standard deviation (sd)above that of the population mean. The preferred source for measuringantibody response in the present invention is milk or blood. “Milk”, asused herein, is meant to include both the milk and the colostrum.

[0076] The term “greater than average immune response” as used hereinmeans a measure of an indicator of cell mediated immune responsecombined with the indicator of the change in antibody response, whichtogether provide a value that is greater than approximately one standarddeviation above that of the population mean.

[0077] The term “population” as used herein refers to a group of animalsof the same species in which the measurements are obtained. Forinstance, in the examples of the present invention, three differentgroups or herds are used to obtain the population data. Population asused herein can also refer to a sample of the population, in so far asobtaining the ranking of immune response in a significant sample of apopulation can enable one to estimate or predict the immune responseranking of other related animals within the population.

[0078] “Productivity” as used herein, refers to the rate of growth of ananimal including the time to reach a selected market weight, feedconversion efficiency, and reproductive performance including the numberof live animals/litter, and the number of undeformed animals per litter.

[0079] “Stress” as defined herein, is any acute or chronic increase inphysical, metabolic, or production related pressure to the animal. It isthe sum of the biological reactions to any adverse stimulus, physical,metabolic, mental or emotional, internal or external, that tends todisturb an organisms homeostasis. Should an animal's compensatingreactions be inadequate or inappropriate, stress may lead to variousdisorders. Many events can place an animal under stress. These include,but are not limited to: disease, weaning, castration, dehorning,branding, social disruption, change in ration, temperature exercise andparturition. Examples of social disruption include, but are not limitedto: change of location, shipping, and addition or removal of animalsfrom immediate environment. The onset of parturition (also known as“prepartum”), parturition and after parturition (also known as“postpartum”), herein collectively referred to as “periparturition” or“peripartum”, are also known causes of stress in animals. The time ofperiparturition, the time around parturition, is hereinafter referred toas the “peripartum period”. In cows the peripartum period is from aboutthree weeks before to about three weeks after parturition. Therefore, incows, about 8 weeks prior to parturition would be prior to onset of theperiparturition stress; about 3 weeks prior to parturition to about 3weeks postparturition would be during the periparturition stress; andafter about 3 weeks postparturition would be after the peripartumstress.

[0080] Methods of the Invention

[0081] As hereinbefore mentioned, the present invention is directed to amethod of ranking the immune response of an animal within a populationof animals. Further the present invention is directed to a method ofcalculating a mathematical index of the immune response in an animal.The present invention is also directed to the use of the methods of theinvention to decrease the incident of disease, to enhance growth hormone(GH) and IGF-1 levels in animals during periods of stress and to breedhigh immune response animals.

[0082] More particularly, the invention is directed to a method ofranking the immune response of a test animal within a population ofanimals under stress comprising: (a) immunizing the animals with atleast one antigen at least once before the onset of the stress; and (b)measuring the antibody response of the animals to the at least oneantigen at least once before the onset of the stress and at least onceduring the stress, wherein a change in antibody response from before theonset of stress to during the stress for the test animal that is greaterthan the average change in antibody response from before the onset ofthe stress to during the stress for the population indicates that theanimal is a high immune responder.

[0083] In a preferred embodiment, each animal is further immunized atleast once during the stress. In yet a further embodiment, the antibodyresponse of the animals of the population is also measured at leasttwice during the stress. In further embodiments of the present inventionthe changes in antibody responses between each measurement are added toprovide a total antibody response and a total antibody response for thetest animal that is greater than an average total antibody response forthe population indicates that the animal is a high immune responder.

[0084] According to another embodiment of the invention, the method ofranking the immune response further comprises exposing the animals of apopulation to an antigen, preferably under stress, which can evoke acell mediated immune response (CMIR), measuring an indicator of the CMIRat least once during the stress and combining it with the measurementfor antibody response, to obtain an immune response, wherein an immuneresponse of a test animal that is greater than the average immuneresponse of the population during stress indicates that the test animalis a high immune responder. Preferably, the CMIR is specific to theantigen. The antigen used to evoke the CMIR is preferably different thanthe antigen used to invoke the antibody response. In preferredembodiments, the antigen which can invoke a CMIR is selected from thegroup consisting of an intracellular organism and a mitogen. When theantigen which can invoke a CMIR is intracellular organism it ispreferably selected from the group consisting of Mycobacterium bovis andMycobacterium phlei. When the antigen which can invoke a CMIR is amitogen it is preferably selected from the group consisting ofconcanavalin A and phytohaemaglutinin.

[0085] Therefore, in another embodiment, the present invention isrelated to a method of ranking the immune response of a test animalwithin a population of animals under stress comprising: (a) immunizingthe animals with at least one antigen at least once before the onset ofthe stress; (b) measuring the antibody response of the animals to the atleast one antigen at least once before the onset of the stress and atleast once during the stress; (c) exposing the animals to an antigenwhich can evoke a cell-mediated immune response (CMIR); and (d)measuring at least one indicator of the CMIR in the animals during thestress, wherein the change in antibody response from before the onset ofstress to during the stress is combined with the indicator to provideand immune response and a test animal having an immune response that isgreater than an average immune response for the population indicatesthat the animal is a high immune responder. In preferred embodiments,the changes in antibody responses between each measurement are added toprovide a total antibody response and the measurement of the indicatoris combined with the total antibody response to provide an immuneresponse and a test animal having an immune response that is greaterthan an average immune response for the population indicates that theanimal is a high immune responder.

[0086] Suitable indicators of CMIR include, but are not limited to: themeasurement of one or more predetermined cytokines [for example, asdescribed in L. T. Jordan et al. “Interferon Induction in SLA-DefinedPigs”, Res. Vet. Sci. 58:282-283, 1995; J. Reddy et al., “ConstructionOf An Internal Control To Quantitate Multiple Porcine Cytokine mRNAs byrtPCR”, BioTechniques 21:868-875, 1996; W. C. Brown et al., “Bovine Type1 And Type 2 Responses”, Vet. Immunl. Immunopath 63:45-55, 1998];measuring delayed-type hypersensitivity (for example as described inMallard, 1992, PCT/CA93/00533); and measuring in vitro lymphocyteproliferation to at least one antigen (for example, as described inMallard B. A. et al., Animal Biotech 1992 ref. PCT/CA93/00533).

[0087] Although the examples below use cows as the animal model, aperson skilled in the art, upon reading this description, wouldunderstand that the present invention could be applied to other animals,preferably animals used for commercial use, such as pigs, poultry, fish,horses, and companion animals such as dogs and cats. Accordingly,“animal” as used herein includes, all members of the animal kingdom. Ina preferred embodiment of the invention, the animals used are from thebovine genus and more preferably are selected from the group consistingof multiparous and primiparous cows. Further, it is understood that whenconducting a method of the invention relatives may be used as the animalto define the rank of other relatives.

[0088] One skilled in the art would appreciate that the gestation perioddiffers between animal species. As such, when peripartum is the stress,such a person upon reading this description would know that the optimumtimes for immunizing and measuring an animal's immune response, asprovided in this description for cows, may have to be adjusted, ifanother animal species is used.

[0089] In one embodiment of the invention, pre-peripartum or before theon-set of stress, preferably refers to 2 or more weeks before the onsetof stress. For instance, a person skilled in the art would appreciatethat the actual time an animal is immunized before the onset of stresswill depend on the antigen and animal species used.

[0090] According to one embodiment of the invention, whenperiparturition is the stress and cows are the animals, the animals areimmunized at least once before the stress at about 8 weeks beforeparturition and at least once during the stress at about 3 weeks beforeparturition and at about parturition.

[0091] According to a preferred embodiment of the invention, whenperiparturition is the stress and cows are the animals, the antibodyresponse is preferably measured at about 8 weeks before parturition, atabout 3 weeks before partuition and at about parturition. In a morepreferred embodiment the antibody response is further measured at about3 weeks after parturition and, optionally, at about 6 weeks afterparturition. “At about 8 weeks before parturition”, as used herein,means at 8 weeks before parturition +/−4 days. “At about 3 weeks beforeparturition” , as used herein, means at 3 weeks before parturition +/−4days. “At about parturition”, as used herein, means at or up to one weekafter parturition, but not before parturition. “At about 3 weeks afterparturition”, as used herein means at one week, and preferably at or upto 3 weeks, after parturition +/−4 days. “At about 6 weeks afterparturition”, as used herein means at 6 weeks after parturition +/−4days.

[0092] The antigens are preferably formualted with an Adjuvant or theycan be formulated into a vaccine, such as Ecoli J5, as used in theexamples discussed herein. Examples of other possible vaccine antigensfor use in cows include but are not limited to: Presponse (Merial) andIBR/PI3/BVD/BRSV combination vaccine (Bovilan 4K) etc.

[0093] Measuring the change in antibody responses to the antigen overtime intervals, rather than at a discreet points in time, allowed thepresent inventors to develop a mathematical index which can be used torank the animals. The mathematical index as part of the immunization andmeasurement schedules of the present invention provide a method ofranking the immune response of a test animal within a population ofanimals under stress. The method with the index comprise the following:

[0094] (a) immunizing the animals with at least one antigen at leastonce before the onset of the stress and at least once during the stress;

[0095] (b) measuring the antibody response of the animals to the atleast one antigen at least once before the onset of the stress and atleast once during the stress; and

[0096] (c) calculating a mathematical index of the antibody response,wherein the mathematical index is: y=primary antibody response+secondaryantibody response+tertiary antibody response+quaternary antibodyresponse, wherein

[0097] (i) y is the immune response;

[0098] (ii) the primary response is the difference in antibody quantityat a first time point before the onset of stress and a second time pointduring the stress, wherein the animal is immunized at the first timepoint before the onset of stress;

[0099] (iii) the secondary response is the difference in antibodyquantity at a second time point during the stress and at a third timepoint during the stress, wherein the animal is immunized at the secondtime point during the stress;

[0100] (iv) the tertiary response is the difference in antibody quantityat a third time point during the stress and at a fourth time pointduring the stress, wherein the animal is immunized at the third timepoint during the stress; and

[0101] (v) the quaternary response is the difference in antibodyquantity at a fourth time point during the stress and at a fifth timepoint after the stress;

[0102] wherein with animals exhibiting negative secondary and/ortertiary antibody responses the secondary and/or tertiary antibodyresponses are weighted with a co-efficient greater than 1, and a testanimal having a y value greater than about one standard deviation abovethe average of the y value for the population is a high immuneresponder. The immune response, y, may, in other embodiments of theinvention, be calculated using the primary response only (requiring onlyimmunization before the onset of the stress), the primary plus secondaryresponses and/or the primary plus secondary plus tertiary responses,wherein with animals exhibiting negative secondary and/or tertiaryantibody responses, the secondary and/or tertiary antibody responses areweighted with a co-efficient greater than 1.

[0103] The mathematical index of the total immune response can also beobtained with the method of the present invention, wherein the CMIR isadded to the above-noted equation and results in y=primaryresponse+secondary response+tertiary response+quartenary response+CMIR,wherein “y” is the total immune response of each animal of a population,and test animals having a “y” value greater than about one standarddeviation above the average of the population are high immuneresponders. In a further embodiment, the present invention thereforerelates to a method of ranking the immune response of a test animalwithin a population of animals under stress comprising: (a) immunizingthe animals with at least one antigen at least once before the onset ofthe stress and at least once during the stress; (b) measuring theantibody response of the animals to the at least one antigen at leastonce before the onset of the stress and at least once during the stress;(c) exposing the animals to an antigen which can evoke a cell-mediatedimmune response (CMIR); (d) measuring at least one indicator of the CMIRin the animals during the stress; and (e) calculating a mathematicalindex of the antibody response and CMIR, wherein the mathematical indexis: y=primary antibody response+secondary antibody response+tertiaryantibody response+quaternary antibody response+CMIR, wherein

[0104] (i) y is the immune response;

[0105] (ii) the primary response is the difference in antibody quantityat a first time point before the onset of stress and a second time pointduring the stress, wherein the animal is immunized at the first timepoint before the onset of stress;

[0106] (iii) the secondary response is the difference in antibodyquantity at a second time point during the stress and at a third timepoint during the stress, wherein the animal is immunized at the secondtime point during the stress;

[0107] (iv) the tertiary response is the difference in antibody quantityat a third time point during the stress and at a fourth time pointduring the stress, wherein the animal is immunized at the third timepoint during the stress;

[0108] (v) the quaternary response is the difference in antibodyquantity at a fourth time point during the stress and at a fifth timepoint after the stress; and

[0109] (vi) CMIR is the measurement obtained from at least one method ofdetermining CMIR, wherein with animals exhibiting negative secondaryand/or tertiary antibody responses, the secondary and/or tertiaryantibody responses are weighted with a co-efficient greater than 1, anda test animal having a y value greater than about one standard deviationabove the average of the y value for the population is a high immuneresponder.

[0110] The immune response, y, may, in other embodiments of theinvention, be calculated by combining CMIR with the primary responseonly (requiring only immunization before the onset of stress), theprimary plus secondary responses and/or the primary plus secondary plustertiary responses, wherein with animals exhibiting negative secondaryand/or tertiary antibody responses, the secondary and/or tertiaryantibody responses are weighted with a co-efficient greater than 1.

[0111] In one embodiment the present invention relates to a modificationof the mathematical index in which all phenotypic indicators of immuneresponse are converted to estimated breeding values. The use of thismethod is as previously described and includes: to identify animals withhigh immune response; and allow breeding of animals with increasedaccuracy for inherent increases in immune responsiveness.

[0112] Uses

[0113] The methods of this invention can be used to identify preferredanimals selected from the group consisting of: animals that are lesssusceptible to developing a peripartum disease wherein antibody quantityand quality are relevant host resistance factors; animals that are lesssusceptible to developing a peripartum disease wherein antibody quantityand quality and CMIR mediate broad-based disease resistance; animalswith increased growth hormone; and animals with increased IGF-1 outsidethe peripartum period and with decreased IGF-1 inside the peripartumperiod.

[0114] The methods of the invention may also be used to identify lowimmune responders, those animals having immune responses, as determinedusing a method of the invention, that are lower than that of the averageof the population. Such animals may be useful, for example, indetermining the efficacy of new drugs, vaccines and other treatments. Inparticular, the efficacy of a vaccine, drug or other treatment in ananimal can be determined by administering the vaccine, drug or othertreatment to animals in one or more of the high, average and low groups,and comparing the responses to the vaccine, drug or other treatment inone or more of the high, average and low groups to determine theefficacy of the vaccine, drug or other treatment. The theory being thatif the drug or vaccine works on animals with low immune responses itshould work on animals with higher immune responses. “Drug” as usedherein covers all therapeutic and prophylactic treatments.

[0115] The methods of the invention can also be used to obtain apopulation of animals through traditional hereditary breeding techniquesby calculating estimated breeding values (EBVs) of the indicators ofimmune responsiveness (Veterinary Genetics, F. W. Nicholas, OxfordScience Publications, 1987; D. S. Falconer. An introduction toquantitative genetics. Longman, London, 1981), preferably cows, whichare high, average or low immune responders.

[0116] The methods of the invention can also be used to predict orestimate the immune response ranking of an animal by having knowledge ofthe immune response ranking of at least one of the animal's relatives.Factors which would increase the accuracy of the estimate or predictionof such an immune response ranking of an animal, include but are notlimited to: (i) Degree of separation from the animal (the knowledge ofthe ranking of the animal's full siblings and parents would result in abetter estimate than with knowledge of the ranking of only cousins orpartial siblings); (ii) The amount of data (the greater the database ofknowledge of the ranking of one's relatives, the better the estimate orprediction); and (iii) The similarity of environmental factors.

EXAMPLES

[0117] Experimental Design

[0118] Identifying variation in immune response traits during theperipartum period, and any association with disease or production traitsis the first step toward breeding dairy cows with superior healthattributes. To evaluate phenotypic variation in peripartum antibody andcell-mediated immune responses of dairy cows, a total of 136 Holsteindairy animals (88 cows and 49 heifers) from 2 research herds (Herd 1,n=32, 6 heifers and 26 cows; Herd 2, n=67; 34 heifers and 33 cows) and 1commercial herd (Herd 3, n=37, 8 heifers and 29 cows) were examinedweekly from dry-off (approximately eight weeks prepartum; wk-8) to sixweeks postpartum (wk 6). To stimulate specific antibody response duringthe peripartum period, all cows and heifers received intramuscular (im)injections of a mastitis endotoxemia preventive vaccine, an Rc mutant ofEscherichia coli O111:B4 (Rhône Mérieux Escherichia coli J5, RhôneMérieux, Lenexa, Kans.) with the manufacturer's adjuvant. In addition,cows were simultaneously administered ovalbumin antigen (OVA, Type VII,Sigma Chemical Co., St. Louis, Mo.) approximately 8 weeks (4 mg) and 3weeks (2 mg) prior to predicted calving dates. At parturition (wk 0),cows received an additional immunization of the OVA dissolved inphosphate buffered saline (PBS—0.1 M, pH 7.4) (2 mg, im). Peripheralblood was sampled via tail venipuncture at weeks −8, −3, 0, 3, 6, and 9relative to parturition, and centrifuged to monitor serum IgG_(1&2), aswell as specific antibody responses to OVA and J5 E. coli. Colostrum andmilk samples were also collected to measure specific antibody to OVA andtotal IgG₁ and IgG₂ in whey. Colostrum was collected at the firstmilking following parturition. Milk samples were stripped from allquarters approximately 2-4 hr after morning milking. Colostrum and milksamples were stored frozen without preservative at −20° C. until time ofwhey separation and Ig quantification.

[0119] In order to evaluate delayed type hypersensitivity (DTH) as ameasure of cell-mediated immune (CMI) response a subset (n=36) of cowsfrom research Herd 2 (Ponsonby Research Station, Elora, Ontario; n=15cows and 21 heifers) were given a 1.5 mg/mL intradermal injection of theBacillus Calmette Guerin (BCG; Connaught, Mississauga, Ontario) vaccinein the left caudal tail fold at wk 1 postpartum. At wk 3 postpartum,animals that had received the BCG vaccine were given a 0.1 mL (250 USTuberculin Units) intradermal injection of the purified proteinderivative (PPD) of Mycobacterium tuberculosis and 0.1 mL of the control(PBS), in the right caudal tail fold. These sites were locatedproximally to one another, about 4 cm apart. Injection sites in the leftand right caudal folds were located approximately the same distance fromthe base of the tail head (10 cm) and across from one another. Doubleskinfold thickness was measured at 48 and 72 hours using Harpenden SkinCalipers (John Bull, England). As a measure of peripartum lymphocyteproliferation, lymphocytes were harvested from whole blood at weeks −3,0, 3, and 6 relative to parturition and cultured with OVA antigen (5μg/mL) and the T-cell mitogen concanavalin A (Con A; 5 μg/mL).

[0120] Production Data

[0121] Production data were obtained through monthly reports from theOntario Dairy Herd Improvement Corporation (Ontario DHIC). All monthlymilk samples were tested by the Central Milk Testing Laboratory, Guelph,Ontario, for SCC, and compositional content (fat %, protein %). Inaddition, milk samples from cows in research Herd 1 (Shurgain ResearchFarm, Burford, Ontario; n=26 cows and 7 heifers) were tested weekly byOntario DHI. Projected 305 day production parameters for milk, fat, andprotein were used as a measure to compare production between cows fromthe three herds investigated. Three hundred and five day (305-day)projections were calculated based on at least 100 days in milk (DIM).This allows comparisons between cows which may not be at the same stageof lactation when a monthly milk test is taken and between animals withvarying lactation lengths.

[0122] Disease Data

[0123] Occurrence of infectious and metabolic diseases were investigatedthroughout the study period. All disease events were recorded by theherd manager. If an animal had two or more of the same disease event, itwas recorded as one event for the study period.

[0124] Specific Antibody Quantification by Enzyme Linked ImmunosorbentAssay (ELISA)

[0125] Anti-OVA antibody

[0126] Serum was separated from coagulated peripheral blood bycentrifugation (700× g, 15 min) and stored frozen (−20° C.) until timeof assay. Milk samples were centrifuged twice (11000× g, 15 min) toseparate fat from whey. Whey was stored frozen at −20° C. Antibody toOVA was detected by ELISA according to the procedure described byBurton, et al., 1993. Dynatech Immulon II flat bottom 96-wellpolystyrene plates (Fisher Scientific, Don Mills, Ont.) were coated witha 3.11×10⁻⁵ M solution of OVA (OVA, Type VII, Sigma Chemical Co., St.Louis Mo.) dissolved in carbonate-bicarbonate coating buffer (pH 9.6).Plates were incubated (4° C., 48 h), then washed with PBS and 0.05%Tween 20 (Fisher Scientific, Don Mills, Ontario) wash buffer, (pH 7.4)using a EL403 plate washer (Biotek, Mandel Scientific, Guelph, Ontario).Plates were then blocked with a PBS-3% Tween 20 solution and incubated(rt, 1 h). Plates were washed and diluted test sera (1/50 and 1/200) ormilk whey (Neat, 1/10, 1/100 and 1/400) and controls were added usingthe quadrant system described by Wright (1987). After blocking, serasamples were added in duplicate, and milk whey samples were added inquadruplicate. Plates were incubated (rt, 2 h). Subsequently, alkalinephosphatase conjugate rabbit anti-bovine IgG (whole molecule) (SigmaChemical Co., St. Louis, MO) was dissolved in wash buffer, added to theplates and incubated (rt, 2 h). P-Nitrophenyl Phosphate Disodium tablets(pNPP) (Sigma, St. Louis, Mo.) were dissolved in a 10% diethanolaminesubstrate buffer, (pH 9.8). Plates were washed with wash buffer, pNPPwas added to the plates and then incubated (rt, 30 min). Plates wereread on a EL311 automatic ELISA plate reader (BIO-TEK Instruments,Highland Park, Vermont) and the optical density (OD) was recorded at 405and 630 nanometres (nm) when the positive control reached OD≧0.999. The630 filter was used as a reference filter to correct for fingerprintsand irregularities in the plastic of the plates. The mean of the numberof replicates added to each plate was corrected to an OD=1.0 bymultiplying by the inverse of the mean of the positive controls.Corrected means of each dilution were then added together to give anadditive OD value, indicative of antibody response.

[0127] Negative and positive controls included a pooled sample ofpre-immunization sera and a pooled sample of sera from cows 14 days postsecondary immunization, respectively. Sera from 20 animals was tested byELISA to determine antibody responses at 4 dilutions (1/50, 1/200,1/800, and 1/3200). The dilutions 1/50 and 1/200 provided responses withminimal prozone which corresponded to anticipated antibody responsecurve kinetics based on the immunization schedule, and allowed a cleardifferentiation between positive and negative controls. Since for asmall subpopulation of cows these dilutions exhibited some prozoneeffects, the dilutions were added together to provide an index ofantibody response. Similarly, in order to determine the optimal sampledilutions that would be used to quantify antibody in milk whey, milkfrom two cows was serially diluted (neat, 1/2, 1/4 . . . 1/512) todetermine the dilution which had a minimal prozone, and allowed optimaldifferentiation of responses of positive and negative control sera.Acceptable dilutions included Neat, 1/10, 1/100 and 1/400. Thesedilutions were added together to give and index of whey antibodyresponse.

[0128] Anti-E.coli Antibody

[0129] Lyophilized E.coli J5 (American Type Culture Collection,Rockville, Md., USA) was grown in 5 mL Tryptic Soy Broth (TSB) for 2days to obtain log phase growth. This culture was then transferred to a1 L flask of sterile TSB and sealed aseptically. The culture wasincubated (37° C., 12 hrs, 200 rpm) on an INNOVA platform shaker (NewBrunswick Scientific, Edison, N.J.). A 1 mL sample of cells was dilutedlogarithmically and plated on blood agar to determine the colony formingunit count (cfu). The number of cfu was 1.13×10⁹. Live cells were thenpelleted by centrifugation (5000 g, 15 min). Cells were washed in PBSand pelleted by centrifugation 3 times (first wash, 5000× g, 15 min;second and third washes, 7500× g, 15 min) Cells were suspended in PBS ata final volume of 1 L. The culture was then heat-killed by boiling for 2hours. The final preparation was diluted until an absorbance reading=1.0at 540 nm was obtained. The E.coli J5 was stored frozen (−20° C.) untiltime of assay.

[0130] Serum was separated from coagulated peripheral blood bycentrifugation (700× g, 15 min) and stored frozen (−20° C.) until timeof assay. According to the method described by Rhône-Mérieux AnimalHealth (Lenexa, Kans.; 1994 personal communication), heat-killedEscherichia coli strain J5 (ATCC, Rockville, Md.) was coated at aconcentration of 6.25×10⁷ cfu per mL onto Dynatech Immulon IIpolystyrene 96 well flat bottom plates overnight at 4° C. After washingwith wash buffer (PBS plus 0.05% Tween 20), 1% gelatin was added toblock non-specific binding and plates were incubated (rt, 1 h). Plateswere washed and four replicates of test serum (dilutions of 1/1000,1/1500, 1/2000 and 1/2500) were added using a modified quadrant system(Wright, 1987). One column with PBS-05% Tween 20 was used as a blank,one column of fetal calf serum (FCS, Bockneck Laboratories, Can Sera,Rexdale, Ontario, Canada) was used as a negative control and one columneach of the negative and positive controls prepared from pooled pre- andpost immunization sera were plated, respectively. Test sera wereincubated (rt, 2 h), and then the plates were washed with PBS−0.05%Tween 20. Horseradish peroxidase conjugate goat anti-bovine IgG wholemolecule in PBS (1/4000) (The Binding Site, Birmingham, England) wasadded and the plates were incubated (rt, 1 h). After subsequent washingwith PBS−0.05% Tween 20, the substrate,2,2′-azino-di-(4-ethyl-benzthiazoline sulphonate-6) (ABTS) was added andplates were incubated (rt, 30 min). Plates were then read on an EL311automatic ELISA plate reader (BIO-TEK Instruments, Highland Park, Vt.)and the OD was recorded at 405 nm and 490 nm. The mean OD of the foursample replicates were corrected for each plate by multiplying by theinverse of the mean of the positive controls and used as an indicator ofantibody response. Based on the immunization protocol and phenotypicobservation of antibody response curve kinetics of all dilutions tested,the 1/1000 dilution consistently allowed for differentiation betweenpositive and negative controls, exhibiting minimal prozone effect.Therefore 1/1000 was the dilution of choice for comparison betweenanimals.

[0131] The same pooled positive sera used in the OVA ELISA was tested toensure a differentiation between pre-immune negative sera and postsecondary immunization sera. This positive control was determined to besuitable for this assay since an OD of 1.0 was reached at a dilution of1/200 while the negative sera had an OD that was <0.100. Negativecontrol sera in this assay was prepared by absorbing boiled whole cellE.coli J5 in pooled non-vaccinated sera. FCS was also used as a negativecontrol. Quantification of Immunoglobulin G_(1&2) by RadialImmunodiffusion (RID)

[0132] Quantification of Total IgG_(1&2) in sera

[0133] Radial immunodiffusion (RID) was used according to a modifiedmethod described by Mallard et al, 1992, to determine the concentrationsof IgG_(1&2) in serum at weeks 0, 3, and 6 relative to parturition.Immunodiffusion medium was prepared by dissolving 2% Seakem agarose (FMCBioproducts, Mandel Scientific, Guelph, Canada) and 2% PolyethyleneGlycol 8000 (Carbowax 8000, Fisher Scientific, Fairlawn, N.J.) in PBS.Rabbit anti-bovine isotype specific IgG_(1&2) (VMRD, Pullman, Wash.) wassuspended in the immunodiffusion agarose at a concentration of 33%(vol/vol) for IgG₁ and 30% (vol/vol) for IgG₂. Immunodiffusion mediumwas held in a liquid state and poured into 5 mL immunodiffusion plates.Agarose was allowed to solidify and then three rows of wells, 6 wellsper row, were punched with a 3 mm glass pipette tip. Standardconcentrations of IgG₁ (1800 mg/100 mL) and IgG₂ (1600 mg/100 mL) ascontrols (VMRD, Pullman, Wash.) were diluted (neat, 1/2, 1/4, 1/8, 1/16,1/32) and five microliters of these standard serial dilutions were addedto the top row of each plate. Five μL of each test sample was added tothe two bottom rows of each plate. Plates were incubated (rt, 20 h) in ahumidified chamber. Afterwards, ring diameters were measured using acalibrated grid held over a fluorescent light source. Ring diametersfrom standards were used to make a standard curve for each platedetermined by linear regression. By plotting ring diameter on the x axisand the log of the concentration (mg/100 mL) on the y axis, theconcentration of Ig could be determined.

[0134] Quantification of Total IgG_(1&2) in Whey

[0135] In order to determine Ig concentration in colostrum,immunodiffusion medium was prepared by dissolving 2% Seakem agarose (FMCBioproducts, Mandel Scientific, Guelph, Canada) and 2% PolyethyleneGlycol 4000 (Carbowax 3350, Fisher Scientific, Fairlawn, N.J.) in PBS.Rabbit anti-bovine isotype specific IgG_(1&2) (VMRD, Pullman, Wash.) wassuspended in the immunodiffusion agarose at a concentration of 33%(vol/vol) for IgG₁ and 30% (vol/vol) for IgG₂. The procedure for thepreparation of RID medium and plates for colostral whey samples wasessentially the same as that described for sera except that polyethyleneglycol 3350 was used instead of 8000 to improve ring clarity. Colostrumsamples were centrifuged twice (11000× g, 15 min) to separate fat fromwhey prior to plate application.

[0136] In order to determine Ig concentration in milk, immunodiffusionmedium was prepared by dissolving 2% Seakem agarose and 2% PolyethyleneGlycol 4000 (Carbowax 3350, Fisher Scientific, Fairlawn, N.J.) in PBS.Rabbit anti-bovine isotype specific IgG₁ (VMRD, Pullman, Wash.) wassuspended in the immunodiffusion agarose at a concentration of 12.5%(vol/vol). Milk samples were centrifuged twice (11000× g, 15 min) toseparate fat from whey. The procedure for the preparation of RID mediumand plates for milk whey samples is essentially the same as thatdescribed for colostrum except that the concentration of goat-antibovinesera suspended in the immunodiffusion media was 33% for IgG₁ and 30% forIgG2. Whey from wk 3 was tested for both IgG_(1&2) subclasses. At wk 6however, IgG₁ only was tested in whey since very low concentrations ofIgG₂ exist in normal milk.

[0137] Examination of the Cell-mediated Immune Response (CMIR)

[0138] Delayed Type Hypersensitivity

[0139] A preliminary study was conducted to determine if the Ponsonbyherd was previously exposed to Mycobacterium tuberculosis or other crossreactive antigens from Mycobacterium paratuberculosis. Five cows and sixheifers were injected intradermally with 0.1 cc of the PPD of M.tuberculosis (Connaught, Mississauga) and a control dose of 0.1 cc PBS(pH 7.4) in the right caudal tail fold located proximally to one another(approx. 4 cm apart) PPD was injected in a designated area above the PBSsite. Both injection sites were 10 cm from the base of the tail head.Prior to injection, injection sites were encircled with a colouredmarker and a pre-test and pre-control thickness measurement was taken intriplicate, using Harpenden skin calipers (John Bull, England). Thismeasurement was identified as the time=zero hours measurement. After 24and 48 hours, skin thickness measurements were taken to assess thepercent increase in skin thickness of control and test sites. It wasdetermined that the herd had not previously been exposed to the M.tuberculosis antigen since 95% of all the animals tested had very littleor no increase in skin thickness at the injection sites (i.e a 0-7%increase in skin thickness) and that the BCG/PPD test system would besuitable to measure DTH responses in this herd.

[0140] Two animals from the Ponsonby herd were selected to determine theoptimal time point following the injection of PPD that would yield amaximal response and ensure that actual DTH responses were induced.Animals were evaluated at 0, 6, 12, 24, 48 and 72 hours post PPDchallenge. Measurements taken at 6 to 12 hours were used to ensure thatthe response to antigen was not characteristic of an antibody-mediatedreaction. In cattle, the maximal response to PPD is normally observedaround 72 hours (Radostits et al, 1990). Preliminary results indicatedthat the response was optimal at 48 hours, therefore both time pointswere evaluated for comparison between animals.

[0141] Prior to immunization using PPD, and a PBS control, a pre-testand pre-control (at time=0 hours) skin thickness measurement wasobtained in triplicate from each of the 36 animals evaluated. Fortyeight and 72 hours after secondary challenge, these measurements weretaken again. The amount of skin thickness increase at 48 and 72 hoursexpressed as a percent increase in skin thickness was calculated asfollows:

% increase in skin thickness=(((A−B)/B)−(C−D)/D)))×100

[0142] where

[0143] A=mean test thickness (at time=48, 72 hours),

[0144] B=mean of pre-test thickness (at time=0 hours),

[0145] C=mean of control thickness (at time=48, 72 hours),

[0146] D=mean of pre-control thickness (at time=0 hours).

[0147] Cows could be classified according to their % increase in skinthickness as either non-responsive or low responders (less than one sdbelow the mean), moderate responders (between one sd below and one sdabove the mean), or high responders (more than one sd above the mean).

[0148] Lymphocyte Proliferative Response

[0149] Lymphocyte proliferation assays were performed according to theprocedure of Chang, et al. (1993). Peripheral whole blood wascentrifuged (850× g, 15 min) and buffy coats were diluted in phosphatebuffered saline (PBS 0.1M, pH 7.4). Peripheral blood lymphocytes (PBL)were separated by density gradient centrifugation (1000× g, 30 min) ofbuffy coats using aqueous Histopaque 1.077 (Sigma Chemical Co. St.Louis, Mo.) Cell pellets were washed by centrifugation in PBS (400× g, 7min) and suspended in a volume of culture medium (Rosewell Park MemorialInstitute; RPMI-1640, and 100 I.U. penicillin-streptomycin, prepared byCentral Media Laboratory; Ontario Veterinary College, University ofGuelph, Guelph, Ontario.) and 10% FCS and brought to a finalconcentration of 2.0×10 ⁶ cells/mL in culture medium. In order todetermine specific clonal proliferative responses to antigen, a stocksolution (50 μg/mL) of OVA (Sigma Chemical Co., St. Louis Mo.) dissolvedin RPMI-1640 was prepared and stored in small aliquots at −70° C. Fiveug/mL of OVA was added to 6 replicates of test lymphocytes in 96 wellflat-bottom plates (Nunc, Fisher Scientific, Don Mills, Ontario). Mediumwas added to 6 well replicates of cells as non-stimulated controls andthis represented background or unstimulated cell proliferation. As ageneral indicator of lymphocyte proliferation, Con A mitogen similarlyprepared from stock solution (50 ug/mL) and diluted (5 μg/mL) was addedto 6 replicates of cells on a separate plate containing an additional 6wells as medium controls. Following 24 h of incubation with OVA or ConA(37° C., 6% CO₂) cells received an 18 h ‘pulse’ incubation with 0.5 μCimethyl tritiated thymidine per well (ICN Biochemical, Canada Ltd.Montreal, PQ). Plates were frozen until cells were harvested using aplate harvesting system (LKB Wallac, Turku, Finland) onto fiberglassfilter mats (LKB Wallac, Turku, Finland). Radioactivity was recorded ascounts per minute (cpm) of test minus non-stimulated controls ofretained radioactivity measured by a beta plate liquid scintillationcounter (LKB Wallac,Turku, Finland).

[0150] OVA antigen preparations were tested using the above describedmethod at a concentration of 5 μg/mL, 10 μg/mL, and 20 μg/mL. Althoughlymphocyte proliferative responses did not differ significantly betweenthe tested concentrations, 5 μg/mL was selected to induce PBLproliferation in subsequent assays. To determine the concentration ofthe mitogen able to induce optimal PBL proliferation, Con Aconcentrations were tested at 2 μg/mL, 5 μg/mL and 10 μg/mL. Five μg/mLyielded maximal lymphocyte proliferative responses and was thereforeselected as the concentration applied in further investigations.

[0151] Flow Cytometric Assay for the Detection of CD Surface Moleculeson Lymphocytes Either Not Stimulated or Stimulated with Con A or OVA

[0152] In order to determine which lymphocyte subsets were present afterstimulation with either Con A or OVA, cells were stained with monoclonalantibodies recognizing 5 cell surface markers according to the methoddescribed by Van Kampen and Mallard (1997). The monoclonal antibodiesused in this study were kindly provided by Dr. Jan Naessens of ILRAD(Institute for Animal Health, Compton, Berkshire) and includedantibodies to the following cell surface markers: CD2+ (IL-A43), CD4+(IL-A11), CD8+(IL-A105), WCI (IL-A29), and IgM (IL-A30). A subset ofanimals (n=10) from research Herd 2 (Ponsonby, Elora, Ontario; n=7) andthe commercial herd (Speedvalley Holsteins, Fergus, Ontario;n=3) wereevaluated for expression of these lymphocyte cell surface markers atweeks −3, 0, 3, and 6 relative to parturition. Lymphocytes were preparedand cultured as previously described for lymphocyte proliferationassays, however, each 96 well plate was divided into quadrants each with24 wells. Twenty four replicates each of Con A stimulated, OVAstimulated (at 5 μg/mL and 20 μg/mL) and non-stimulated controls werecultured for 42 hours (the same total duration used in the lymphocyteproliferation assays). After 42 hours, cells were harvested by pipette,washed with PBS and transferred to 10 mL glass test tubes. Cells werecentrifuged (400× g, 10 min), and supernatants were poured off and cellswere resuspended in 250 μL PBS +0.1M Azide. Immunostaining was performedin 96-well round-bottom plates (Corning, New York, N.Y.). Fifty μL ofcells and 50 μL of diluted primary antibody were added to each well andincubated (20 min, rt). After incubation, 100 μL of PBS+0.1M sodiumazide (Fisher Scientific, Fairlawn, N.J.) was added to each well to washthe cells. Cells were suspended by mixing on a shaker and centrifuged(400× g, 6 min). Supernatants were then removed using an aspirator. Thiswashing procedure was performed twice. Fifty μL of FITC-conjugated goatanti-mouse IgG(H+L) (Cedarlane Laboratories, Hornby, Ontario) was thenadded to the cells and cells were incubated (rt, 20 min). Afterincubation, plates were washed twice as described above. Cells werefixed in 1% paraformaldehyde and transferred into 3 mL polystyrene tubes(Becton Dickinson, Lincoln Park, N.J.) containing 300 μL of 1%paraformaldehyde. Tubes were covered with parafilm and refrigerateduntil time of assay.

[0153] A FACS Scan flow cytometer (Becton Dickinson, Lincoln Park, N.J.)was used to acquire all lymphocyte subset data. LYSIS II software(Becton Dickinson, Lincoln Park, N.J.) was used for fluorescence dataanalyses. Lymphocytes were gated from other populations based on theirforward and side scatter characteristics. Five FITC histograms wereplotted for each cow, time point and culture condition observed.Histograms representing fluorescence of cells expressing CD2 (pan Tcell), CD4 (helper T cells), CD8 (cytotoxic/suppressor T cells), WCI (gdT cells), and IgM (B cells) cell surface markers were examined. Theregion of background fluorescence was established with the negativecontrol marker, M1. Everything to the right of this marker wasconsidered positive.

[0154] Complete Blood Cell Counts

[0155] Complete Differential Blood Cell Counts were determined by theClinical Pathology Laboratory at the Ontario Veterinary College,University of Guelph, Guelph, Ontario, Canada. Counts included thepercent and number erythrocytes, banded neutrophils, segmentedneutrophils, lymphocytes, monocytes, basophils, eosinophils, as well astotal leukocytes.

[0156] Somatic Cell Counts in Milk

[0157] Weekly somatic cell counts (SCC) of the Shurgain herd wereobtained using the weekly sampling service offered by Ontario DHI.Weekly samples of cows in the Ponsonby and Dunk herds sampled 1-4 hoursafter morning milking were tested for SCC by the Mastitis Laboratory atthe Ontario Veterinary College, University of Guelph, Guelph, OntarioCanada. Monthly somatic cell counts were obtained from Ontario DHIrecords for all three herds.

[0158] Categorization of Cows Based on Antibody Response

[0159] Biological Classification Using Antibody Response Curves

[0160] Serum antibody responses to OVA from the first herd investigated(Shurgain, Burford, Ontario, n=32) were graphed individually for eachcow at weeks −8, −3, 0, 3, and 6 to examine response curve patterns.Evaluation of these curves during the peripartum period through to peaklactation indicated that enough variation existed to rank animalsaccording to antibody response to OVA. Cows that showed consistentlyabove average responses to OVA were categorized as high or Group 1. Cowsthat had an average antibody response up until parturition andthereafter showed a lack of measurable (LOM) antibody response werecategorized as the post-partum LOM response group or Group 2. Cows thathad an average antibody response until three weeks pre-partum and showeda progressive decline in measurable antibody response were categorizedas the peripartum LOM group or Group 3.

[0161] Subsequent investigations of immune responses between cows in theother herds studied revealed similarities. However, subtle differencesin the amplitude and direction of antibody response curves, in relationto the immunization schedule, indicated that the data was continuous innature. Thus, the groups determined for Herd 1 wouldn't necessarilyapply to all herds. It was clear then, that antibody responses to OVAwere on a continuum, and any classification method implemented wouldbenefit from a quantitative approach to readily and appropriatelypartition phenotypic variation between cows.

[0162] Quantitative Classification Using a Mathematical Index

[0163] Serum antibody responses to OVA were evaluated over timeintervals, rather than discrete points in time. Individual animalantibody response curves from week −8 to week 6 relative to parturition(week 0) were dissected into components reflecting the response toantigen following immunizations. Primary response was defined as thechange in antibody to OVA from week −8 to week −3 relative toparturition following primary immunization at week −8 (Primary=OD valueat week-3 minus OD value at week −8). Secondary response was defined asthe change in antibody to OVA from week −3 to parturition followingsecondary immunization at week −3 (Secondary=OD value at week 0 minus ODvalue at week −3). Tertiary response was defined as the change inantibody to OVA from parturition to week 3 following tertiaryimmunization at parturition (Tertiary=OD at week 3 minus OD at week 0).Quaternary response was defined as change in antibody to OVA from week 3to week 6 (Quaternary=OD value at week 6 minus OD value at week 3).Quaternary response was included to observe the change in antibodyresponse between the end of the immediate postpartum period (wk 3) andpeak lactation. These responses were added together to give an index ofantibody response to OVA between wk −8 and wk +6 relative to parturitionas follows:

y _(index)=primary+secondary+tertiary+quaternary

[0164] where,

[0165] y=total antibody response;

[0166] primary, secondary, tertiary, and quaternary responses are aspreviously defined;

[0167] primary, secondary, tertiary, and quaternary responses whenpositive, have an equal weight of 1.

[0168] Animals which exhibited negative secondary or tertiary responsesduring the immediate pre-and postpartum period were weighted with acoefficient of 1.5 instead of 1. Only secondary and tertiary responseswere weighted in this manner, since this is the period when lowered hostresistance mechanisms are thought to contribute to increased occurrenceof disease. The coefficients for weighting negative secondary andtertiary responses were optimized using the original biologicalassessment for grouping animals in the first herd investigated. Thequantitative ranking of animals had to reflect the biological assessmentof grouping animals based on the magnitude and direction of response toimmunization.

[0169] The mean of the antibody response index was determined andanimals that exceeded one standard deviation above the mean wereclassified as high responders (Group 1). Animals that were one standarddeviation below the mean were classified as low responders (Group 3).Animals with an index of antibody response that ranged between onestandard deviation below and above the mean were classified as averageresponders (Group 2).

[0170] Statistical Methods

[0171] Least squares analysis of variance (ANOVA) and corrected means(least square means, LS Means) were generated using the General LinearModels (GLM) Procedure of the Statistical Analysis System (Helwig andCouncil, 1982). A model was constructed for the following dependentvariables: antibody response to OVA in sera and whey, antibody responseto E.coli in sera, concentration of IgG_(1& 2) in serum and whey,background lymphocyte proliferation and lymphocyte proliferationfollowing culture with Con A or OVA, DTH, SCS and production variables.Sources of variation included in the model for each dependent variableare summarized in Table 1. Data that did not show a normal distribution,as indicated by the univariate procedure of SAS (Helwig and Council,1982), were transformed to natural logarithms. The Proc CORR procedureof SAS was used to generate Pearson product moment correlationcoefficients between immune response parameters and productionvariables. Results were considered to be statistically significant ifthe p-value was ≦0.05 and trends were reported at a p-value≦0.10.

[0172] Models indicated are base models. Some parameters were excludedif non-significant in order to generate LS Means.

[0173] Model 1

[0174] Antibody Response to OVA in Serum and Whey, Ig in Serum and Wheyand E. coli in Serum $\begin{matrix}{y_{ijklmn} = \quad {\mu + {herd}_{i} + {{season}\text{-}{yr}_{j}} + {{cow}( {{group}^{*}{parity}} )}_{klm} +}} \\{\quad {{week}_{n} + {group}_{k} + {parity}_{1} + ( {{group}^{*}{parity}} )_{kl} +}} \\{\quad {( {{group}^{*}{week}} )_{kn} + e_{ijklmno}}}\end{matrix}$

[0175] where,

[0176] y_(ijklmno)=observed response of cow m in group k and parity 1for each sample week of each cow,

[0177] μ=the population mean,

[0178] herd_(i)=fixed effect of herd (i=1,2,3),

[0179] season-yr_(j)=fixed season-year effect (j=Spring 1994, Summer1994, Fall 1994, Winter 1994/1995, Spring 1995, Summer 1995, Fall 1995,Winter 1995/96),

[0180] group_(k)=fixed effect of group based on antibody response to OVA(k=1,2,3),

[0181] parity_(l)=fixed effect of parity (l=1,2, or >3),

[0182] (group*parity)_(kl)=fixed effect of group*parity interaction,

[0183] cow(group*parity)_(klm)=random effect of cow-grouped withingroup*parity term,

[0184] week_(n)=fixed effect of sample week (n=−8, −3, 0, 3, 6, 9),(group*week)_(kn)=fixed effect of group by week interaction term;

[0185] e_(ijklmno)=random or residual error term.

[0186] When parity was not significant, the cow term was edited toreflect the appropriate nested variable

[0187] Model 2

[0188] Cell Mediated Immune Responses and Lymphocyte Proliferation$\begin{matrix}{y_{ijklmnop} = \quad {\mu + {herd}_{i} + {{season}\text{-}{yr}_{j}} + {{cow}( {{group}^{*}{parity}} )}_{klm} +}} \\{\quad {{week}_{n} + {group}_{k} + {parity}_{1} + ( {{group}^{*}{parity}} )_{kl} +}} \\{\quad {( {{group}^{*}{week}} )_{kn} + {replicate}_{o} + {b({cov})}_{ijklmno} + e_{ijklmnop}}}\end{matrix}$

[0189] where all variables are as described for model 1 except,

[0190] y_(ijklmnop)=observed response of cow m in group k and parity lfor each replicate o at each sample week,

[0191] replicate_(o)=fixed effect of replicate (o=1,2,3,4,5,6), and

[0192] b(cov)ijklmno=regression coefficient of y_(ijklmnop) on restingcell proliferation for the klm^(th) cow

[0193] The model for DTH was:

[0194] y_(ij)=μ+group_(i)+e_(ij);

[0195] where,

[0196] μ=the population mean,

[0197] group_(i)=fixed effect of group based on antibody response to

[0198] OVA (i=1,2,3),

[0199] e_(ij)=random or residual error term.

[0200] Parity was not included in this model since it was notsignificant, and when included with group, did not provide enoughdegrees of freedom to run the analysis of variance.

[0201] Tests of hypothesis of group or parity were tested against the MSrandom error term for cow. Type III Sums of Squares corrected for allother variable within the model were used account for the variation inimmune responses.

[0202] The following non-limiting examples are illustrative of thepresent invention:

Example 1

[0203] Periparturient Antibody Response Profiles of Holstein Cows: AnInitial Immunobiological Assessment

[0204] To evaluate phenotypic variation in peripartum immuneresponsiveness of dairy cattle, 33 Holstein cows were immunized withovalbumin (OVA) and Escherichia coli J5 at weeks −8 and −3 prior toparturition. At parturition (week 0), cows received an additionalimmunization of OVA. Blood was collected at weeks −8, −3, 0, 3 and 6relative to parturition to measure serum immunoglobulin (Ig)concentration, and antibody to OVA and E.coli. Colostrum and milk werealso collected post-parturition to measure Ig and antibody to OVA. Allcows had a measurable antibody to OVA following primary immunization,but not all cows responded to second and/or third immunizations.Antibody response to OVA was used to classify cows into three groupsrecognizing animals with sustained measurable antibody response beforeand after parturition (Group 1), animals which responded poorly or didnot respond to immunization at parturition (Group 2), and animals whichdid not respond to immunizations at week −3 or at parturition (Group 3).

[0205] The objectives of this example were threefold: 1) to investigateantibody response during the peripartum period; 2) to classify cowsbased on variation of antibody response; and, 3) determine if antibodyresponse is associated with the occurrence of disease.

[0206] Materials and Methods

[0207] Animals and Treatments

[0208] Antibody response of 33 Holstein cows were examined fromapproximately eight weeks prepartum (week −8), based on predictedcalving dates to six weeks postpartum (week 6). Twenty-six animals weremultiparous cows and seven were primiparous heifers. Cows received anintramuscular (im) injection of a mastitis endotoxemia preventivevaccine with the manufacturer's adjuvant (Rhône Mérieux E. coli J5,Rhône Mérieux, Lenexa, Kans.) along with the antigen OVA (Type VII,Sigma Chemical Co., St. Louis, Mo.), at weeks −8 (4 mg OVA) and −3 (2 mgOVA). At parturition (week 0), cows received an additional immunizationof OVA without adjuvant dissolved in phosphate buffered saline (PBS—0.1M, pH 7.4) (2 mg, im). Ovalbumin was chosen as an inert soluble antigento which these animals had likely not been previously exposed. E. coliJ5 was used as a complex, insoluble, biologically relevant antigen towhich most dairy cows were likely to have been previously exposed.Antibody response to OVA was used to classify cows into three groupsrecognizing animals with sustained measurable antibody response beforeand after parturition (Group 1), animals which responded poorly or didnot respond to immunization at parturition (Group 2), and animals whichdid not respond to immunizations at week −3 or at parturition (Group3)(FIG. 1A).

[0209] Blood and Milk Sampling Schedule

[0210] Blood was collected by tail venipuncture at week −8, and weeklyfrom weeks −3 to 6 relative to parturition. Serum was used to monitorimmunoglobulin G_(1&2) concentrations, and determine antibody to OVA andE. coli J5. Colostrum and milk were collected to determine antibody toOVA and to monitor IgG₁ (weeks 0, 3, 6) and IgG₂ (weeks 0 and 3)concentration. Colostrum was collected at the first milking followingparturition. Milk was obtained from all quarters approximately 2-4 hrafter morning milking. Colostrum and milk samples were frozen withoutpreservative at −20° C. until the time of whey separation and analysis.

[0211] Anti-OVA Enzyme Linked Immunosorbent Assay (ELISA)

[0212] As described in the General Methods section.

[0213] Anti-E. coli J5 ELISA

[0214] As described in the General Methods section.

[0215] Radial Immunodiffusion Assay

[0216] As described in the General Methods section.

[0217] Disease Occurrence

[0218] As described in the General Methods section.

[0219] Milk Somatic Cell Count

[0220] Milk (AM/PM composite sample) was collected weekly by the herdmilker during milking to determine somatic cell count (SCC). Only SCCwhich coincided with the day of blood sample collection for each weekare reported. SCC, an indicator of subclinical mastitis, was transformedto somatic cell score (SCS) for analysis. SCS is the natural logarithmof SCC in cells/μL and is calculated as follows (Shook, 1993):

SCS=log_(e)(SCC/100)÷log^(e)(2)+3

[0221] Statistical Methods

[0222] Type III least squares analysis of variance (ANOVA) and correctedmeans (least square means, LS Means) were generated using the GeneralLinear Models (GLM) Procedure of the Statistical Analysis System (SAS;Helwig and Council, 1982). The statistical models used included fixedeffects of antibody response groups (1, 2, 3), cow nested withinantibody response group, and week relative to parturition (weeks −8, −3,0, 3, and 6). In preliminary analysis, the effect of parity was notsignificant and was therefore removed from all subsequent models. Amodel was constructed for the following dependent variables: antibodyresponse to OVA in sera and whey, antibody response to E. coli J5 insera, and the concentration of IgG_(1&2) in serum and whey. Sources ofvariation included in the model for each dependent variable aresummarized in Table 1. Data that were not normally distributed asindicated by the univariate procedure of SAS, were transformed tonatural logarithms. (whey antibody to OVA, serum antibody to E. coli,serum and whey IgG₂. Pearson product moment correlation coefficientsbetween immune response variables were generated using the correlationsprocedure of SAS (Proc CORR). Results were considered to bestatistically significant if the P-value was ≦0.05 and trends werereported at P-values ≦0.10.

[0223] Results

[0224] Antibody Response to OVA

[0225] Antibody in Serum

[0226] Serum antibody to OVA varied significantly over the peripartumperiod and individuals could readily be classified into three immuneresponse groups: high responders (Group 1, n=12; 6 heifers, 6 cows)versus animals which exhibited a LOM response to immunization eitherpostpartum (Group 2, n=12 cows) or pre- and postpartum (Group 3, n=9; 8cows, 1 heifer). Approximately 1/3 (Group 1) of the animals hadconsistently above average serum antibody response to OVA followingimmunization at weeks −8, −3, and 0 relative to parturition. Theremaining animals had OD values measuring antibody to OVA that wereclose to the population mean or had responses lower than the populationmean and did not respond following immunization at week −3 orparturition (FIG. 1A). All cows, including those of Group 3, had serumantibody greater than background (week −8) at week −3 and therefore wereconsidered low responders rather than non-responders. The statisticalmodel (ANOVA) accounted for 94.19% of the total variation in serumantibody to OVA over the peripartum period. Effects of cow (P≦0.0001),antibody response group (P≦0.0001), week (P≦0.0001), and the interactionbetween antibody response group and week (P≦0.0001), contributedsignificantly to the variation in serum antibody to OVA (Table 1).

[0227] Antibody in Whey

[0228] Cow (P≦0.0001), week (P≦0.0001), and antibody response group(P≦0.0001) contributed significantly to the variation in antibody inwhey (Table 1). There was also a tendency for the interaction betweenantibody response group and week (P≦0.09) to account for variation inwhey antibody to OVA. Population LS Means of whey antibody to OVAdeclined significantly following parturition, such that at week 0 the ODvalue was 1.456 compared to 0.645 (P≦0.004) at week 3 and 0.366±0.20(P≦0.0001) at week 6 (FIG. 1B). At weeks 3 and 6, Group 1 cows weresignificantly higher than (P≦0.05) Group 3 cows.

[0229] Antibody Response to E. coli J5

[0230] Cow (P≦0.0001), week (P≦0.0001), and antibody response group(0.0001) all contributed significantly to variation in antibody responseto E. coli J5. OD values of pre-immunization sera (week −8) indicatedthat these cows had minimal measurable E. coli J5 antibody (populationmean of OD=0.296; n=33) compared to post-vaccination antibody at week −3(0.739) and week 0 (0.789). Antibody response to E. coli J5 waspositively correlated with antibody response to OVA (r²=0.59, P≦0.0001).

[0231] IgG₁ &IgG₂ in Serum, Colostrum, and Milk

[0232] Antibody response group significantly contributed to thevariation of serum IgG₂ (P≦0.0001) only. Group 3 cows had asignificantly (P≦0.05) higher serum IgG₂ concentration than Groups 1 and2 at parturition. Antibody to OVA was negatively and significantlycorrelated with serum IgG₂ (r=−0.23; P≦0.05).

[0233] Disease Occurrence

[0234] Fifty four and a half % of the 33 animals evaluated wereconsidered healthy during this study. Of the diseased animals, sevencows had mastitis (21.21%), seven had ketosis (21.21%) and three cowshad other diseases (9.09%). Animals in Group 1 that had above averageantibody to OVA, had the lowest percent occurrence of disease (17%)(FIG. 2) and actually had no clinical mastitis.

[0235] 3.5. Somatic Cell Score (SCS)

[0236] At parturition, LS Means of SCS were significantly lower (P≦0.05)for Group 2 cows (SCS=3.2) compared to Group 1 (SCS=4.36) and Group 3(SCS=4.98) cows. At weeks 2, 3, 4, and 6 after parturition, all groupsdiffered significantly from one another, and, Group 1 cows consistentlyhad the lowest SCS while Group 3 cows consistently had the highest SCS.

[0237] Discussion

[0238] Antibody response before and after parturition has not beenthoroughly investigated. Antibody response to OVA, a test antigen towhich these animals would normally not have and had probably not beenpreviously exposed, was utilized to partition cows into three immuneresponse groups recognizing animals with sustained antibody responsebefore and after parturition (Group 1), animals which did not respond toimmunization at parturition (Group 2), and animals responding poorlythroughout the peripartum period (Group 3). Variation in antibodyresponse to E. coli J5, a biologically relevant antigen, was moredifficult to partition. Pre-immunization E. coli antibody wassignificantly lower compared to post immunization antibody in this herd.This indicates that the E. coli J5 antigen would be useful forclassifying animals in the herd evaluated according to their antibodyresponse but does not indicate that another herd will respond in thesame way. Pre-immunization antibody may be higher in other herds wheregram negative bacteria are frequently encountered.

[0239] Nagahata et al. (1992), examined B lymphocyte populations inorder to evaluate host defense in dairy cows during the periparturientperiod. This study found no significant changes in the number of Blymphocytes of cows from two weeks before until two weeks afterparturition. However, they did report a significant decrease in antibodyproducing cells immediately after parturition. The authors suggestedthis indicated a decrease in B lymphocyte function during the immediatepostpartum period. This is consistent with the low peripartum antibodyresponse seen in some animals in the present study.

[0240] Although it has been reported that serum antibodies decline atparturition and colostral antibodies increase due to the sequestrationof immunoglobulins into the mammary gland (Detilleux et al., 1995), thisstudy suggests that lower antibody in serum does not necessarily relateto Ig transport. For instance, Group 1 cows, which had the highest serumantibody responses, also tended to have higher whey antibodies to OVApostpartum, when compared to cows of Groups 2 and 3. Initially, it wasquestioned whether low serum antibody may be associated with higherantibody in the colostrum or milk. This data indicates that animals withhigh serum antibody also supply high concentrations of antibody to themammary gland.

[0241] This example has demonstrated significant individual variationduring the peripartum period and confirms that not all cows havedepressed antibody response. In swine, animals with inherently high andlow immune response phenotypes can also be identified in a population(Mallard et al., 1992). In light of previously reported heritability(h²) estimates of bovine antibody response (Burton et al., 1989), thesedata from this study may suggest that Group 1 animals could beinherently better able to produce antibody, in spite of the metabolicand physical stresses of the peripartum period. Cows in Group 1 did havethe lowest occurrence of peripartum disease, particularly mastitis (0%occurrence), and significantly lower SCS scores following parturitionthan cows in Groups 2 and 3, thus indicating that antibody responseshould be considered as a potential marker of peripartum diseaseresistance.

Example 2

[0242] A Quantitative Approach to Classifying Holstein Dairy Cows Basedon Antibody Response, the Relationship Between Antibody Response andPeripartum Disease Occurrence, and Heridability Estimates

[0243] A quantitative approach was developed to partition phenotypicvariation of peripartum antibody response profiles of Holstein cows andto determine associations with peripartum mastitis. Using a mathematicalindex, 136 cows and heifers from three herds were ranked as highresponders (Group 1), average responders (Group 2) or low responders(Group 3) to OVA. Grouping animals by serum antibody response to OVAindicated that animals ranked similarly for antibody to OVA in whey andantibody to Escherichia coli in serum. Differences in serum and wheyIgG₁ concentrations between antibody response groups were notsignificant. Serum IgG₂ concentration however, varied between group,within herd and across time. Whey IgG₂ did not differ significantlybetween antibody response groups within herd. Occurrence of mastitis wasnegligible for Group 1 animals. In contrast, Group 1 animals from Herd2, had the greatest occurrence of mastitis while Group 3 had the lowest.Milk somatic cell score (SCS), was lowest for Group 1 animals in Herd 1and lowest for Group 3 animals in Herd 2, thus supporting thedistribution frequency of clinical mastitis in those herds. Herd 3 SCSdid not differ significantly between antibody response groups and didnot underscore the distribution of clinical mastitis.

[0244] The objective of this study was to confirm the existence of highand low antibody response profiles amongst individuals across threeherds and to devise a method for quantitatively classifying cows intogroups based on antibody response to standardized immunizationprotocols. Relationships were evaluated between antibody response,immunoglobulin concentration, milk somatic cell score, and diseaseoccurrence with respect to antibody response group.

[0245] Materials & Methods

[0246] Animals and Treatments

[0247] Antibody responses of 136 Holstein dairy cows and heifers from 2research herds (Herd 1, n=32, 6 heifers and 26 cows; Herd 2, n=67; 34heifers and 33 cows) and 1 commercial herd (Herd 3, n=37, 8 heifers and29 cows) were examined from eight weeks prepartum (week −8) based onpredicted parturition dates to six weeks postpartum (week 6). Forty nineanimals were primiparous heifers, 47 animals were in their secondlactation and 41 were multiparous cows (>2 lactations). Antibodyresponses were evaluated as previously described (Mallard et al., 1997;Ch. V ). Animals received an intramuscular (im) injection of ovalbumin(OVA; Type VII, Sigma Chemical Co., St. Louis, Mo.) and a mastitisendotoxemia preventive vaccine with the manufacturer's adjuvant (RhôneMérieux E. coli J5, Rhône Mérieux, Lenexa, KS) at weeks −8 (4 mg) and −3(2 mg). At parturition (week 0), animals received an additionalimmunization of OVA in phosphate buffered saline (PBS—0.1 M, pH 7.4) (2mg, im). OVA was chosen as an inert test antigen to which these animalshad not likely been previously exposed. E. coli J5 was used becausedairy cows could be expected to have been previously exposed to E. coli,a complex antigen, having biological relevance.

[0248] Blood and Milk Sampling Schedule

[0249] Blood was collected by caudal tail venipuncture at approximatelyweek −8 relative to parturition, and weekly from weeks −3 to 6 relativeto parturition. Samples were used to monitor serum immunoglobulinG_(1&2) and serum antibody to OVA and E. coli J5. Colostrum and milksamples were collected to monitor whey IgG_(1&2) and antibody to OVA inwhey. Colostrum was collected at the first milking followingparturition. Milk samples were stripped from all quarters approximately2-4 hr after morning milking. Colostrum and milk samples were storedfrozen without preservative at −20° C. until time of whey separation andimmunoglobulin quantification.

[0250] ELISA for OVA Antibody Detection In Serum and Whey

[0251] Antibody to OVA was detected by ELISA, and quantified based onoptical density measurements according to a procedure previouslydescribed (Mallard et al., 1997; Ch. V). Sera samples (weeks −8, −3, 0,3, and 6) diluted 1/50 and 1/200 were assayed in duplicate. Whey samples(weeks 0, 2, 3, 4, and 6) diluted 1/10, 1/100, 1/400 and undiluted wereassayed in quadruplicate.

[0252] ELISA for E. coli J5 Antibody Detection In Serum

[0253] Antibody response to E. coli J5 was measured according to themethod previously described (Mallard et al., 1997; Ch. V). Serum samples(weeks −8, −3, 0, 3, and 6) diluted 1/1000 were assayed inquadruplicate.

[0254] Radial Immunodiffusion Assay

[0255] Radial immunodiffusion was used according to the method describedby Mallard et al. (1992) to determine the concentrations of serumIgG_(1&2) at weeks 0, 3, and 6 and whey IgG₁ at weeks 0, 3, and 6 andwhey IgG₂ at weeks 0 and 3.

[0256] Quantitative Classification of Animals Based on Antibody Response

[0257] Serum antibody responses to OVA were evaluated over timeintervals, rather than discrete points in time. Individual animalantibody response curves from week −8 to week 6 relative to parturition(week 0) were dissected into components reflecting the response toantigen following immunizations. Primary response was defined as thechange in antibody to OVA from week −8 to week −3 relative toparturition following primary immunization at week −8 (Primary=OD valueat week−3 minus OD value at week −8). Secondary response was defined asthe change in antibody to OVA from week −3 to parturition followingsecondary immunization at week −3 (Secondary=OD value at week 0 minus ODvalue at week −3). Tertiary response was defined as the change inantibody to OVA from parturition to week 3 following tertiaryimmunization at parturition (Tertiary=OD at week 3 minus OD at week 0).Quaternary response was defined as change in antibody to OVA from week 3to week 6 (Quaternary=OD value at week 6 minus OD value at week 3).Quaternary response was included to observe the change in antibodyresponse between the end of the immediate postpartum period (wk 3) andpeak lactation. These responses were added together to give an index ofantibody response to OVA between wk −8 and wk +6 relative to parturitionas follows:

y _(index)=primary+secondary+tertiary+quaternary

[0258] where,

[0259] y=total antibody response;

[0260] primary, secondary, tertiary, and quaternary responses are aspreviously defined;

[0261] primary, secondary, tertiary, and quaternary responses whenpositive, have an equal weight of 1.

[0262] Animals which exhibited negative secondary or tertiary responsesduring the immediate pre-and postpartum period were weighted with acoefficient of 1.5 instead of 1. Only secondary and tertiary responseswere weighted in this manner, since this is the period when lowered hostresistance mechanisms are thought to contribute to increased occurrenceof disease. The coefficients for weighting negative secondary andtertiary responses were optimized using the original biologicalassessment for grouping animals in the first herd investigated. Thequantitative ranking of animals had to reflect the biological assessmentof grouping animals based on the magnitude and direction of response toimmunization.

[0263] The mean of the antibody response index was determined andanimals that exceeded one standard deviation above the mean wereclassified as high responders (Group 1; n=18). Animals that were onestandard deviation below the mean were classified as low responders(Group 3; n=23). Animals with an index of antibody response that rangedbetween one standard deviation below and above the mean were classifiedas average responders (Group 2; n=95).

[0264] Heritability Estimates

[0265] Sire and error variance components of serum antibody to OVA wereestimates by REML using Variance Component Estimation (VCE) software(Groeneveld, E. 1994). Sire and error variances were used to estimatepaternal half-sib heritabilities for serum antibody to OVA at weeks −8,−3, 0, 3, and 6 relative to calving. Approximate standard errors werecomputed from the variance covariance matrix of sire and error variancecomponent estimates.

[0266] Mastitis Occurrence

[0267] Occurrence of clinical mastitis was recorded throughout the studyperiod by herd managers. Two or more events of mastitis it were recordedas one event for the study period (Martin et al., 1993). Incidence ofmastitis occurrence was calculated by dividing the number of animalswithin an antibody response group that had at least one disease event byall the animals in that antibody response group, and multiplying thisnumber by 100. Mastitis occurrence was evaluated for associations withantibody response group within each herd, using odds-ratio (OR) (Martinand Meek, 1987). Odds-ratios in this study was calculated on a withinherd basis, as the ratio between the rate of mastitis in one antibodyresponse group versus the rate of mastitis in the rest of the herd (i.e.the other two groups). Odds-ratio is the approximate relative risk whenthe rate of disease in the population is relatively infrequent (<5%)(Martin and Meek, 1987). Odds ratios values were tested for significanceusing the chi-square test (Martin and Meek, 1987).

[0268] Milk Somatic Cell Count

[0269] Milk (AM/PM composite sample) was collected weekly to determinesomatic cell count (SCC), an indicator or subclinical mastitis. Only SCCwhich coincided with blood sample collection for each week were used inevaluation. SCC was transformed to somatic cell score (SCS) foranalysis. SCS is the natural logarithm of SCC in cells/mL and iscalculated as follows:

SCS=log_(e)(SCC/100)÷log_(e)(2)+3 (Shook, 1993)

[0270] Statistical Methods

[0271] Type III least squares analysis of variance (ANOVA) and correctedmeans (least square means, LS Means) were generated using the GeneralLinear Models (GLM) Procedure of the Statistical Analysis System (SAS;Helwig and Council, 1982) to evaluate the effects of herd, season-year,cow, antibody response group, parity, week, and their interaction termson antibody response to OVA and E. coli, and immunoglobulinconcentration (Table 2). Tests of hypothesis of main effects were testedagainst the MS for cow. Sources of variation that were not significantwere removed from the model in order to generate LS Means. Data that didnot show a normal distribution (E. coli antibody response, serum Igq andwhey IgG₂) as indicated by the univariate procedure of SAS (SAS, 1982),were transformed to natural logarithms. LS means were converted back tooriginal units from log_(e) transformed data. Consequently, standarderrors of means are not shown. The Proc CORR procedure of SAS was usedto generate Pearson product moment correlation coefficients betweenimmune response parameters. Results were considered to be statisticallysignificant if the p-value was <0.05 and trends were reported at thep-value <0.10.

[0272] Results

[0273] Serum Antibody to OVA

[0274] Cow, antibody response group, week, and the interaction betweenantibody response group and week contributed to the variation (P≦0.0001)in serum antibody to OVA (Table 2). Herd did not significantlycontribute to the variation in serum antibody to OVA. As expected, therank of antibody response to OVA was Group 1>Group 2>Group 3 except atweek −8 prior to immunization and significant differences were notedbetween all groups at weeks −3, 0, 3, and 6. Population LS meanssignificantly (P<0.0001) increased from pre-immunization (week −8) toweek −3 (post primary immunization) in all antibody response groups andOVA antibody response varied significantly across time at weeks −3, 0,3, and 6. (FIG. 3).

[0275] Heritability Estimates of Antibody to OVA

[0276] Heritability estimates (h²) of antibody to OVA at weeks −8, −3,0, 3, and 6 relative to calving were 0.64, 0.62, 0.32, 0.50, and 0.58respectively. Standard errors could not be calculated by VCE softwaredue to the small sample size evaluated.

[0277] These heritability estimates can be used to obtain an EstimatedBreeding Value (EBV) of an animal in accordance with the proceduredescribed in PCT/CA93/00533 to Wilkie et al., filed Dec. 9, 1992,entitled “Methodology For Developing A Superior line of DomesticatedAnimals” (also see, Veterinary Genetics, F. W. Nicholas, Oxford SciencePublications, 1987; D. S. Falconer, An introduction to quantitativegenetics, Longman, London, 1981). EBV is an indicator of an animal'sinherent ability to produce an immune response and its ability to passgenes influencing these traits to offspring. For the purposes of thepresent invention, EBV values are useful in selecting animals to be bredin order to produce offspring which inherit the level of ability toproduce a high immune response when under stress. High immune responsemay, in part, influence disease resistance.

[0278] Whey Antibody to OVA

[0279] Herd contributed significantly (P<0.01) to variation in antibodyresponse to OVA and therefore, herds were further analyzed separately.Cow, antibody response group, and week all significantly contributed tothe variation in antibody to OVA in whey (P<0.0001); however, there wasno significant contribution of the interaction term antibody responsegroup and week to the variation in response. For all herds, antibody toOVA in whey by antibody response group, ranked similarly to the antibodyresponses observed for serum, such that Group 1>2>3. This was consistentfor colostral and milk whey from parturition until week 6 of lactation(FIGS. 4A,B, and C). Least squares means of antibody to OVA in whey forall herds declined significantly from parturition to peak lactation.Correlation analysis between antibody to OVA in sera with antibody toOVA in whey, indicated a positive and significant relationship for Herd1 (r=0.45; P<0.0001), Herd 2 (r=0.28; P<0.001) and Herd 3 (r=0.45;P<0.001) respectively.

[0280] Antibody to E. coli J5 in Sera

[0281] Herd contributed significantly (P<0.003) to variation in antibodyresponse to E. coli J5 and therefore, herds were further analyzedseparately (Table 2).

[0282] Herd 1

[0283] Cow, antibody response group, and week each significantly(P<0.0001) contributed to the variation in antibody to E. coli J5.Although antibody OD was not significantly different between antibodyresponse groups from week −3 to week 6, the rank of LS Means of antibodyresponse to E. coli by antibody response group was Group 1>Group 2>Group3 (FIG. 5A). Least squares means of antibody to E. coli J5 varied duringthe peripartum period (week −3 to week +3) and up to peak lactation(week +6) and were significantly higher (P<0.0001) than pre-immunizationantibody at week −8 for all animals, regardless of group (ODvalue=0.275) (FIG. 5A). Correlation analysis, comparing antibody to E.coli J5 with antibody to OVA in sera, indicated a positive andsignificant relationship (r=0.56; P<0.0001). The correlation betweenserum anti-OVA and E. coli for Group 1, 2, and 3 was 0.66 (P<0.001),0.59 (P<0.0001) 0.38 (P<0.06), respectively.

[0284] Herd 2

[0285] Cow, antibody response group by parity, parity and weeksignificantly contributed to the variation in antibody response to E.coli J5 (P<0.0001) for Herd 2. Antibody for Group 3 animals at week −8was significantly higher (OD value=0.386) than for animals of Group 1(OD value=0.257; P<0.005) and Group 2 (OD value=0.292; P<0.05). Opticaldensity values of antibody to E. coli for animals in Groups 1 and 2 fromweek −3 to week 6 was similar to OD values of serum antibody to OVA.Optical density values of antibody were consistently positive followingthe immunization but were not significantly higher than the populationmean. In contrast to serum antibody to OVA, Group 3 animals had ODvalues that were consistent but not significantly lower than thepopulation mean. (FIG. 5B). Least square means of antibody response toE. coli J5 at weeks −3, 0, 3, and 6 were significantly higher (P<0.0001)than pre-immunization antibody at week −8 regardless of group (ODvalue=0.307). Correlation analysis between serum antibody to E. coli J5and serum antibody to OVA indicated a positive and significantrelationship (r=0.49; P<0.0001). The correlation between antibody to E.coli J5 and antibody to OVA for Groups 1, 2, and 3 was 0.65 (P<0.0001),0.54 (P<0.0001), and 0.31 (P<0.08) respectively.

[0286] Herd 3

[0287] Cow grouped within antibody response group, antibody responsegroup, week, and the interaction between week and antibody responsegroup significantly contributed to the variation in antibody to E. coliJ5 (P<0.0001) in Herd 3. In this herd, antibody for Group 1 animals wassignificantly lower (P<0.05) at weeks −8 and −3 compared to Group 2 and3 animals. At parturition, Group 1 and 2 animals had higher antibody toE. coli than Group 3 animals. At weeks 3 and 6, however, the rank ofantibody response group for antibody to E. coli was similar to the otherherds, in that Group 1>Group 2>Group 3 (FIG. 5C). LS Means of antibodyto E. coli J5 at weeks −3, 0, 3, and 6 were significantly differentacross time and were significantly higher (P<0.0001) thanpre-immunization antibody regardless of group (OD value=0.224)(FIG. 5C).Correlation analysis between serum antibody to E. coli J5 and serumantibody to OVA indicated a positive and significant relationship (0.47;P<0.0001). Correlation between serum antibody to E. coli J5 and antibodyto OVA for Groups 1, 2, and 3 were 0.93 (P<0.007), 0.48 (P<0.0001), and0.36 (P<0.006) respectively.

[0288] IgG₁in Serum and Whey

[0289] Analysis of variance indicated that the effect of weekcontributed significantly (P<0.05) and the effect of antibody responsegroup tended (P<0.07) to contribute to variation in serum IgG₁. Exceptat week 3, serum IgG₁ did not differ significantly between groups;however, Group 1 animals tended to have lower serum IgG₁ compared toanimals of Group 2 and 3. Least square means of total IgG₁ in seraincreased significantly (P<0.0001) from parturition (430.09 mg/100 mL)to week 3 (687.46 mg/100 mL) and week 6 (799.51 mg/100 mL) (FIG. 6A,population mean). Correlations between serum IgG₁ concentration andserum antibody to OVA and E. coli were not significant.

[0290] The effects of week and parity contributed significantly (P<0.05)to the variation in IgG₁ concentration in whey. Although antibodyresponse group did not significantly contribute to variation in wheyIgG₁ (FIG. 6B), LS means of IgG₁ concentration (mg/100 mL) at week 0were significantly lower for Group 1 (768.16 mg/100 mL) and Group 3(1081.39 mg/100 mL) compared to Group 2 (1381.60 mg/100 mL).Concentration of IgG₁ did not differ significantly between groups atweeks 3 and 6. Population LS Means of IgG₁ concentration in wheydeclined significantly from parturition (1046.28 mg/mL) to week 3 (44.93mg/100 mL, P<0.0001). There was no significant change at week 6 (43.25mg/100 mL). Correlation analysis between whey IgG₁ concentration andwhey antibody to OVA indicated a positive and significant relationship(r=0.711 ; P<0.0001). The correlation coefficients between whey IgG₁ andwhey OVA antibody response for Groups 1, 2, and 3 were 0.52 (P<0.0001),0.76 (P<0.0001), and 0.69 (P<0.0001), respectively.

[0291] G₂ in Sera

[0292] Herd contributed significantly (P<0.0001) to variation in serumIgG₂ concentration and therefore, herds were analyzed separately.

[0293] Herd 1

[0294] Effects of cow and the interaction between antibody responsegroup and week contributed significantly (P<0.05) to variation in IgG₂concentration for Herd 1. Antibody response group did not significantlycontribute to the variation in IgG₂ ; however, LS means of IgG₂ in seraat weeks 0 and 3 was lowest for Group 1 animals and highest for Group 3animals. This trend reversed at week 6, such that Group 1 animals hadthe highest concentration of IgG₂ and Group 3 animals had the lowest. LSMeans of IgG₂ significantly increased from 1019.43 mg/100 mL atparturition to 1534.56 mg/100 mL at week 3 but declined significantly atweek 6 to 1103.23 mg/100 mL. Correlation analysis, between antibody toOVA in sera and concentration of IgG₂, indicated a negative andsignificant relationship (r=−0.23, P<0.03). Correlations betweenantibody to OVA with serum IgG₂ concentration indicated for Group 1, 2,and 3 were 0.07 (ns), −0.35 (P<0.004) and −0.33 (ns). Significantcorrelations were not observed between E. coli antibody response andserum IgG₂ concentration, even when examined by group.

[0295] Herd 2

[0296] Cow significantly contributed (P≦0.05) to the variation of serumIgG₂ concentration while antibody response group and the interactionbetween antibody response group and parity tended to contribute to thevariation in serum IgG₂ concentration. At parturition, groups did notsignificantly differ in serum IgG₂. At week 6, LS means of IgG₂concentration for animals in Group 1 were significantly higher than forGroup 3 animals. Least square means of IgG₂ concentration did not differsignificantly between weeks 0, 3, and 6. Correlation analysis betweenserum IgG₂ concentration and serum antibody to OVA indicated a positiveand significant relationship (r=0.15; P<0.03). Significant correlationswere not observed between serum IgG₂ concentration and serum antibody toOVA or serum antibody to E. coli J5.

[0297] Herd 3

[0298] Cow (P<0.005) and parity (P<0.04) contributed significantly tothe variation of serum IgG₂. concentration. Week (P<0.09) tended tocontribute to variation in serum IgG₂ concentration. Antibody responsegroup did not significantly contribute to variation in serum IgG₂concentration. Correlations between serum IgG₂ concentration andantibody to OVA and E. coli were not significant.

[0299] IgG₂ in Whey

[0300] Herd contributed significantly (P<0.03) to the variation in serumIgG₂ concentration and therefore, herds were analyzed separately.

[0301] Herd 1

[0302] Week contributed significantly to variation in IgG₂ concentrationin whey. Whey IgG₂ concentration did not differ significantly betweengroups (FIG. 8A). LS Means of total IgG₂ concentration in whey declinedsignificantly from week 0 (327.34 mg/100 mL) to week 3 (26.31 mg/100mL). Correlation analysis between whey IgG₂ concentration and antibodyto OVA indicated a positive and significant relationship (r=0.7;P<0.0001). Correlations between whey IgG₂ concentration and wheyantibody to OVA were r=0.9 (P<0.002), 0.6 (P<0.0003), and 0.8 (P<0.02)for Groups 1, 2, and 3, respectively.

[0303] Herd 2

[0304] None of the parameters in the linear model contributedsignificantly to variation in whey IgG₂ concentration, and, therefore,LS means were not estimable. Correlations between whey IgG₂concentration and whey antibody to OVA indicated a positive andsignificant relationship (r=0.3, P<0.009). Correlations between wheyIgG₂ and whey antibody to OVA was 0.2 (ns), 0.5 (P<0.0001), and 0.6 (ns)for Groups 1, 2, and 3, respectively.

[0305] Herd 3

[0306] Antibody response group and week significantly contributed to thevariation in whey IgG₂ concentration. Whey IgG₂ concentration did notsignificantly differ between groups at parturition, and responses atweek 3 could only be estimated for Group 3 animals since responses forGroups 1 and 2 were either low or too low to be detected. Correlationanalysis indicated no significant relationships between whey IgG₂concentration and whey antibody to OVA.

[0307] Mastitis Occurrence

[0308] Percent mastitis occurrence varied between groups and betweenherds. Rates of occurrence of clinical mastitis are presented in table3. Mastitis did not occur in Group 1 of either Herds 1 or 3. Mastitisoccurrence in Herd 1 was 21.7% and 33.3% for Groups 2 and 3,respectively. Mastitis occurrence in Herd 3 was 11.5 and 10% for Groups2 and 3 respectively. However, in Herd 2, Group 1 animals had thehighest occurrence of mastitis (15.4%) which exceeded the percentoccurrence of mastitis in Groups 2 (2.1%) and 3 (0%) (FIG. 7). Animalswith mastitis in Herds 1 (n=6 heifers; n=26 cows) and 3 (n=8 heifers;n=29 cows) were in their second or greater parity. Animals with mastitisin Herd 2 (n=34 heifers; n=33 cows) were all heifers. Across all herds,animals in Group 3 had the highest rate of mastitis occurrence (13.6%)compared to Group 1 (11.1%) and Group 2 (9.3%) (Table 3). Thesedifferences across herds however, were not significant.

[0309] Odds-ratio for Mastitis

[0310] Within herd, odds-ratio calculations comparing animals of oneantibody response group with the other two groups indicated that onlyanimals in Group 1 of Herd 2 had a statistically significant higherrelative risk of having a mastitis event (by 7.57 times) compared to theanimals in the rest of the herd. Although the risk of mastitisoccurrence within Group 3 of Herds 1 and 3 was 2.16 and 1.8 timesgreater (respectively) than for other groups, these values were notsignificant.

[0311] Somatic Cell Score

[0312] For Herds 1 and 2, cow, week and antibody response groupsignificantly contributed to the variation in SCS (Table 2). In Herd 3,only the effect of cow within antibody response group accounted for thevariation in SCS. Somatic cell score was significantly different betweengroups in Herds 1 and 2 but not Herd 3. LS Means of SCS in Herd 1 werelowest for the high antibody responder animals, and greatest for the lowantibody responder animals at weeks 3, 4, 5 and 6 following parturition(FIG. 8A). Conversely, LS Means of SCS in Herd 2 were significantlylower for low antibody responder animals compared to high antibodyresponder animals (FIG. 8B).

[0313] Discussion

[0314] Example 1 indicated that animals could be classified according tothe amplitude and direction of their individual OVA antibody responseprofiles, and that this ranking had some association with mastitisoccurrence. This herd, Herd 1 used in Example 1, was evaluated with twomore herds, Herds 2 and 3. The objective of the current study was toverify the relevance of high and low antibody response profiles acrossthe three herds and to determine if it would be possible to develop aquantitative measure of classification for antibody response thatreflected the initial qualitative assessment of animals. The resultsindicated substantial variation in antibody response to OVA from theperipartum period to peak lactation and that animals could be rankedusing a quantitative index. Animals that ranked high, average or low forserum antibody response, also ranked similarly for whey antibody to OVA.Serum antibody to OVA was expected to be significantly different betweengroups since animals were purposefully classified into high or lowgroups based on their total antibody response curve slopes as eitherless or greater than one standard deviation from the population mean ofthe total index of antibody response, but antibody in whey was notclassified in this manner. Whey antibody responses within each herddemonstrated that high and low serum OVA antibody responses were alsohigh and low in whey, respectively.

[0315] In all herds, antibody to the more biologically relevant antigen,E. coli, ranked similarly to the ranking for antibody to OVA,particularly at weeks 0, 3, and 6 after parturition. This can helpidentify animals which respond best following immunization. Nonetheless,ranking based on response to OVA may be preferable since OVA is notnormally encountered in the dairy cow's environment thus eliminating thepossibility of pre-existing antibody to OVA. In theory, any antibody toOVA responses are expected to be evoked only by the immunizationprotocol. Further, antibody to E. coli was significantly affected byherd making comparisons of populations difficult.

[0316] Previous mathematical approaches to assess variation in innateand immune host resistance mechanisms during the peripartum period thathave included work by Detilleux et al. (1994) who used a fittedpolynomial model to assess hyporesponsiveness during the peripartumperiod. These results were utilized in an animal model to detectvariation between daughters of various sire groups. This method ofassessment of hyporesponsiveness was not suitable for this study sinceit requires many data points across time. Variation in antibody to OVAwas partitioned using a simple model wherein animals that had anyhyporesponsiveness in the immediate peripartum period were ranked lowercompared to animals that responded consistently and positively to OVAimmunization.

[0317] Antibody response to ovalbumin OVA in dairy calves has beenreported by Burton et al. (1989) to be heritable (h²=0.48). Thoughstandard errors of sire and error variances could not be calculated,heritability estimates, of serum antibody to OVA were high (h²>0.50) attime points before and after calving. That the heritability estimate atparturition (h²=0.32) was lower than at other time points evaluated, maybe explained by the complex interactions that occur between hormones andthe immune system during the immediate postpartum period. Takentogether, these results indicate a possible significant geneticcomponent to bovine antibody to OVA, although heritability may be lowerat times when the dairy cow experiences the physical and metabolicstresses of parturition and early lactation. These estimates will needto be confirmed on larger populations but suggest that genetic selectionfor increasing antibody responsiveness is possible, if deemedsignificant, in the peripartum cow.

[0318] Correlation analysis between antibody to OVA in serum or whey andIgG_(1&2) by antibody response group indicated some significantrelationships. However, antibody response group in the statistical modeldid not significantly, but tended to contribute to the variation inserum or whey IgG_(1&2). Serum and whey IgG₂ distributions by antibodyresponse group differed for each herd and consequently, significantrelationships between groups that are common to all herds were difficultto determine. Unpublished data from this laboratory and other studieshave indicated that the serum antibody to OVA is largely of the IgG₂subclass (Gilbert et al., 1994) and therefore, may have indicated someassociation between the two parameters investigated. However, since herddifferences existed, it was not feasible to relate previously publishedresults with IgG₂ concentration investigated in the current study.

[0319] The incidence of mastitis by antibody response group was notconsistent between herds. In Herd 1 and Herd 3, the incidence ofmastitis was greatest for animals with low antibody response (Group 3).All animals within these herds that had mastitis were in their second orlater parity. Though not significant, odds-ratio assessment for theseherds indicated that there was a 2.16 and a 1.80 times greater chance ofhaving a mastitis event if animals were classified in Group 3 versusGroups 1 & 2. In contrast, animals from Herd 2 had a very differentdistribution of mastitis occurrence among groups. Animals in Group 1 hadthe greatest rate of mastitis occurrence and according to the odds-ratioparameter, were 7.57 (P<0.05) times more likely to have a mastitisevent. Further, all animals that had mastitis within Herd 2 were firstparity heifers. Differences in herd management and the distribution ofheifers and cows within each herd and antibody response group, may helpexplain the differences in the distribution of mastitis occurrence. Herd1 (n=6 heifers; n=26 cows) and Herd 3 (n=8 heifers; n=29 cows) had agreater ratio of cows to heifers within each antibody response group,while Herd 2 (n=34 heifers; n=33 cows) heifers and cows were more evenlydistributed among all antibody response groups. Previous studies haveacknowledged an increase in the rate of occurrence of mastitis withadvancing parity (Todhunter et al., 1995, and McClure et al., 1994)which may explain the disparity among herds. The unexpected distributionof mastitis in the Herd 2 might further be explained by a more recentinvestigation from Finland (Myllys et al., 1995) which indicated that inwell managed herds with high milk production and low somatic cellcounts, the rate of the treatment of heifers that had a mastitis episodeincreased from 1.8% to 4.4% over an 8 year period. In contrast toclinical mastitis observed in second parity and multiparous cows, thatstudy further indicated that mastitis in heifers only resulted in smallproduction losses, did not pre-dispose heifers to more mastitis or otherdiseases later in lactation, and the recovery rate from mastitis washigh as indicated by a rapid decline in somatic cell counts (SCC)following infection. This may indicate that mastitis in heifers and incows cannot be compared directly. That disease occurrence in this studywas not consistent among herds, may be explained by a number of factorsincluding the relatively small sample size evaluated, environmental(management) differences, distribution of heifers and multiparous cows,and type of mastitis (subclinical vs. clinical, and the infectingpathogen).

[0320] Alterations in antibody response and the incidence of mastitisindicates that immune response phenotype can be a potential phenotypicmarker for disease resistance and/or susceptibility.

[0321] It was determined that sufficient individual variation inantibody response to OVA existed such that animals could be readilyclassified quantitatively into high, average and low response groupsusing a mathematical index based on OD values of antibody responseprofiles from week −3 to week 6 relative to parturition. Detection ofimmune response traits such as antibody response, which associate wellwith disease resistance can provide a useful phenotype to beginselective breeding of dairy cattle for improved inherent immuneresponsiveness and disease resistance.

Example 3

[0322] Relationships Between Cell Mediated Immune Response (CMIR) andAntibody Response in Periparturient Holstein Dairy Cows

[0323] To examine variation in cell mediated immune response (CMI)response as a function of peripartum serum antibody response toovalbumin (OVA), 136 Holstein cows and heifers from three herds wereevaluated from three weeks before parturition to week 6 followingparturition for lymphocyte proliferative responses to OVA andconcanavalin A (Con A), delayed type hypersensitivity (DTH) to purifiedprotein derivative (PPD) of tuberculin, differential complete blood cellcounts, and somatic cell score (SCS). Using a mathematical index,animals were quantitatively classified based on their antibody responsesto OVA into high (Group 1), average (Group 2) or low (Group 3) antibodyresponse phenotypes. Lymphocyte proliferative responses to OVA (r=−0.28;P<0.0001) and Con A (r=−0.14; P<0.0001) were negatively correlated withantibody to OVA. Animals classified as low antibody response (Group 3)had the highest unstimulated and OVA-stimulated lymphocyte proliferativeresponses. Proliferation of unstimulated lymphocyte proliferativeresponses was depressed between week −3 and parturition. Con Astimulated lymphocyte proliferative responses were also depressed atparturition but this was significant (P<0.05) only in Group 1 which hadhigh antibody response to OVA. Although animals exhibiting high and lowDTH response phenotypes could be identified, DTH was not significantlyassociated to anti-OVA response. Delayed type hypersensitivity at 48 and72 hours were negatively and significantly correlated with unstimulated(r=−0.21; P<0.002; r=−0.17; P<0.01) and Con A stimulated (r=−0.29,P<0.0001; r=−0.28, P<0.0001) lymphocyte proliferation, respectively.Lymphocyte number in peripheral blood declined significantly from week−3 to week 0. Milk somatic cell score (SCS) was negatively, andsignificantly, correlated with in vitro lymphocyte proliferativeresponse to OVA in Herd 2 (r=−0.13; P<0.0001) only. SCS was notsignificantly correlated with Con A stimulation. SCS was also negativelyand significantly correlated with DTH at 48 hours post-challenge(r=−0.21; P<0.01). Cumulative results indicate a variety of negativephenotypic associations between measures of antibody response and CMI,and among indicators of CMI. Since both antibody and CMI are importantin host resistance to infectious disease, use of a selection index wouldbe required to simultaneously enhance both parameters, assuming thereare beneficial associations with cow health.

[0324] Introduction

[0325] Innate and immune response mechanisms of dairy cows are impairedduring the peripartum period. Neutrophil function (Detilleux et al.,1995, Kehrli et al., 1989b, Gilbert et al., 1994 and Cai et al., 1988),complement activity (Detilleux et al., 1995), conglutinin concentration(Detilleux et al., 1995), IgG₁ (Detilleux et al., 1995), milk somaticcell count (Shuster et al., 1996) and lymphocyte proliferation (Saad etal., 1989; Kehrli et al., 1989a; Ishikawa, 1987; Kashiwazaki, 1985;Wells et al.1977) are impaired either pre- or postpartum. Someinvestigations however, indicated that not all animals exhibit a periodof hyporesponsiveness, at least with respect to antibody response.Mallard et al. (1997; Ch. V) demonstrated that peripartum antibodyresponses to ovalbumin (OVA) are continuous in nature, and that thisvariability allowed animals to be readily classified as low, average orhigh antibody producers. Further, animals of the high group had lowermastitis occurrence than animals with average and low antibody response(Mallard et al., 1997; Ch. II & V).

[0326] Given that both antibody and cell mediated immune mechanisms areinvolved in response to infectious disease, it is relevant to evaluatethe relationships between antibody and indicators of CMI. Since negativeassociations have been reported between antibody and aspects of CMI,animals categorized on the basis of antibody response to OVA may havethe inverse rank for CMI responses (Biozzi et al., 1972; Arthur andMason, 1986). This would have practical implications if antibodyresponse was proposed as a candidate marker of disease resistance ofdairy cattle. The objectives of this paper were to evaluate CMIresponses with respect to antibody response group and to determine ifany associations exist with SCS as an indicator of udder health.

[0327] Materials and Methods

[0328] Experimental Design

[0329] To evaluate phenotypic variation in CMIR of dairy cattle, 136Holstein animals from two research herds Herds 1 and 2, respectively)and one commercial herd (Herd 3) were examined every three weeks fromweek −3 to six weeks postpartum (week 6). Eighty-eight animals weremultiparous cows and 48 were primiparous heifers. To stimulate immuneresponse during the peripartum period, animals received an intramuscular(im) injection of ovalbumin (OVA, Type VII, Sigma Chemical Co., St.Louis, Mo.) and with a mastitis endotoxemia preventive vaccine, an Rcmutant of Escherichia coli O111:B4 (Rhône Mérieux Escherichia coli J5,Rhône Mérieux, Lenexa, Kans.) approximately eight weeks (4 mg OVA) andthree weeks (2 mg OVA) prior to predicted parturition dates. Atparturition (week 0), animals received a single im immunizationinjection of OVA (2 mg) dissolved in phosphate buffered saline (PBS—0.1M, pH 7.4). Using a mathematical index, animals were classified based onserum antibody to OVA into high (Group 1), average (Group 2) or low(Group 3) response groups (Ch.II). At weeks −3, 0, 3, and 6, PBMC werestimulated in vitro with OVA (5 mg/mL) and concanavalin A (Con A) (5mg/mL), and proliferative response was measured as described below(section 2.4). For lymphocyte proliferative response, week−3 responsesof animals that calved early or later than predicted parturition dateswere adjusted to reflect the true time point evaluated (i.e. week −2 orweek −4). In order to evaluate delayed type hypersensitivity (DTH) as ameasure of CMIR, a subset (n=36; 15 cows and 21 heifers) of animals fromHerd 2 were given a 1.5 mg/mL intradermal injection of the BacillusCalmette Guerin (BCG; Connaught, Mississauga, Ont.) vaccine in the leftcaudal tail fold at week 1 postpartum.

[0330] Delayed Type Hypersensitivity

[0331] Animals vaccinated with BCG (1.5 mg/mL) received a 0.1 mLintradermal injection of the PPD of tuberculin (250 US Tuberculin Units;Connaught, Mississauga, Ont.) and for control, received 0.1 mL injectionof PBS at week 3 int the right caudal tail fold. The PPD was injected ina designated site approximately 4 cm from the PBS designated site andboth were located 10 cm from the base of the tail. Prior to injection,sites were encircled with a coloured marker and double skin thicknessmeasurement was taken in triplicate (time=0), using Harpenden skinthickness calipers (John Bull, England, UK). Forty eight and 72 hoursafter intradermal injection of PPD and PBS, double skin thickness wasmeasured again. Skin thickness increase at 48 and 72 hours wascalculated as follows:

% increase in skin thickness=(((A−B)/B)−(C−D)/D)))×100

[0332] where

[0333] A=mean test thickness (at time=48, 72 hours)

[0334] B=mean of pre-test thickness (at time=0 hours)

[0335] C=mean of control thickness (at time=48, 72 hours)

[0336] D=mean of pre-control thickness (at time=0 hours)

[0337] Prior to conducting these experiments it was confirmed that theherd was tuberculin test negative on the basis of negative results in 10randomly selected animals.

[0338] Lumphocyte Proliferative Response

[0339] Lymphocyte proliferation assays were performed according to theprocedure of Chang et al. (1993). Briefly, blood was centrifuged (850×g,15 min) and whole blood buffy coats were diluted in phosphate bufferedsaline (PBS 0.1M, pH 7.4). Peripheral blood mononuclear cells (PBMCs)were separated from diluted whole blood buffy coats by density gradientcentrifugation (1000× g, 30 min) using aqueous Histopaque 1.077 (SigmaChemical Co. St. Louis, Mo.) Cell pellets were washed by centrifugationin PBS (400× g, 7 min) and suspended in culture medium (Rosewell ParkMemorial Institute; RPMI-1640, and 100 I.U. penicillin-streptomycin,prepared by Central Media Laboratory; Ontario Veterinary College,University of Guelph, Guelph, Ont.) and 10% FCS and brought to a finalconcentration of 2.0×10⁶ cells/mL. To determine specific clonalproliferative responses to antigen, a stock solution (50 μg/mL) of OVA(Sigma Chemical Co., St. Louis, Mo.) dissolved in RPMI-1640 was preparedand stored in small aliquots at −70° C. Five μg/mL of OVA was added toeach of 6 replicates of test PBMC in 96 well flat-bottom plates (Nunc,Fisher Scientific, Don Mills, Ont.). Medium only was added to 6 wellreplicates of PBMC as non-stimulated controls, to obtain backgroundvalues for unstimulated cell proliferation. The mitogen, concanavalin A(Con A; Sigma Chemical Co., St. Louis, Mo.) prepared from stock solution(50 μg/mL) and diluted to (5 μg/mL) for addition was added to 6replicates of cells on a plate with 6 non-stimulated control replicates.Following 24 h of incubation with OVA or Con A(37° C., 6% CO₂) cellswere incubated for 18 h with 0.5 μCi methyl tritiated thymidine per well(ICN Biochemical, Canada Ltd. Montreal, Que.). Plates were frozen untilcells were harvested using a plate harvesting system (LKB Wallac, Turku,Finland) onto fiberglass filter mats (LKB Wallac, Turku, Finland).Radioactivity was recorded as counts per minute (cpm) by a beta plateliquid scintillation counter (LKB Wallac,Turku, Finland).

[0340] Flow Cytometric Assay for the Detection of CD Surface Moleculesof Peripheral Blood Lymphocytes

[0341] Cell phenotypes were characterized after stimulation with eitherCon A or OVA, by staining with monoclonal antibodies recognizing fivecell surface markers as described by Van Kampen and Mallard (1997). Themonoclonal antibodies were kindly provided by Dr. Jan Naessens of ILRI(ILRI, Nairobi, Kenya) and included antibodies to the following bovinecell surface markers: CD2+ (IL-A43), CD4+ (IL-A11), CD8+ (IL-A105), WCI(IL-A29), and IgM (IL-A30). Peripheral blood lymphocytes from a subsetof animals (n=10) from Herd 2 (n=7) and Herd 3 (n=3) were evaluated forthese lymphocyte cell surface markers at weeks −3, 0, 3, and 6 relativeto parturition. Lymphocytes were prepared and cultured as previouslydescribed for lymphocyte proliferation assays, however, each 96 wellplate was divided into quadrants each with 24 wells. Twenty fourreplicates each of Con A stimulated (5 μg/mL), OVA stimulated (at 5μg/mL and 20 μg/mL) and non-stimulated controls were cultured for 42hours (the same total duration used in the lymphocyte proliferationassays). After 42 hours, cells were harvested by pipette, washed withPBS and transferred to 10 mL glass test tubes. Cells were centrifuged(400× g,10 min), and supernatants decanted and cells were resuspended in250 μL PBS +0.1M sodium azide (Fisher Scientific, Fairlawn, N.J.).Immunostaining was performed in 96-well round-bottom plates (Corning,New York, N.Y.). Fifty μL of cells and 50 μL of diluted primary antibodywere added to each well and plates were incubated (20 min, rt). Afterincubation, 100 μL of PBS 0.1M Azide was added to each well to wash thecells. Cells were suspended by mixing on a shaker and centrifuged (400×g, 6 min). Supernatants were then removed using an aspirator. Thiswashing procedure was performed twice. Fifty μL of FITC-conjugated goatanti-mouse IgG(H+L) (Cedarlane Laboratories, Hornby, Ont.) was thenadded to the cells and cells were incubated (20 min, rt). Afterincubation, plates were washed twice as described above. Cells werefixed in 1% paraformaldehyde and transferred into 3 mL polystyrene tubes(Becton Dickinson, Lincoln Park, N.J.) containing 300 μL of 1%paraformaldehyde. Tubes were covered with Parafilm and refrigerated (4°C.) μg/mL until time of assay.

[0342] A FACS Scan flow cytometer (Becton Dickinson, Lincoln Park, N.J.)was used to acquire lymphocyte subset data. LYSIS II software (BectonDickinson, Lincoln Park, N.J.) was used for analyzing data describingthe frequency of positively stained cells. Lymphocytes were gated outfrom other populations based on their forward and side scattercharacteristics. Histograms representing fluorescence of cellsexpressing CD2 (pan T cell), CD4 (helper T cells), CD8(cytotoxic/suppressor T cells), WC1 (γδ T cells), and IgM (B cells) cellsurface markers were plotted for each cow, timepoint, and culturecondition observed. The region of background fluorescence wasestablished with the negative control marker, M¹. Events accumulated tothe right of this marker were considered positive. (Appendix III, FIG.2).

[0343] Complete Blood Cell Counts

[0344] Complete Blood Cell Counts were determined by the ClinicalPathology Laboratory at the Ontario Veterinary College, University ofGuelph, Guelph, Ontario, Canada. Counts included the percent and totalnumber of leukocytes, erythrocytes, banded neutrophils, segmentedneutrophils, lymphocytes, monocytes, basophils, and eosinophils.

[0345] Milk Somatic Cell Counts

[0346] Weekly milk somatic cell counts (SCC), an indicator ofsubclinical mammary gland infection, were obtained from animals of Herd1 using the weekly sampling service offered by the Ontario Dairy HerdImprovement Corporation (Ontario DHI). Weekly samples of animals in Herd2 and Herd 3 sampled 1-4 hours after morning milking were tested for SCCby the Mastitis Laboratory at the Ontario Veterinary College, Universityof Guelph, Guelph, Ontario Canada. Monthly SCC were obtained fromOntario DHI for all three herds. Somat cell counts were transformed tosomatic cell score (SCS) for analysis. Somatic cell score is thelog-linear transformation of SCC in cells/mL and is calculated asfollows:

SCS=log_(e)(SCC/100)÷log_(e)(2)+3 (Shook, 1993)

[0347] Statistical Methods

[0348] Type III least squares analysis of variance (ANOVA) and correctedmeans (least square means, LS Means) were generated using the GeneralLinear Models (GLM) Procedure of the Statistical Analysis System (SAS;Helwig and Council, 1982) to evaluate the effects of herd, season-year,cow, antibody response group, parity, week, and their interactions onlymphocyte proliferation to OVA and Con A, DTH, complete blood cellcounts and SCS (Table 4). Sources of variation were tested against themean square (MS) for cow grouped within antibody response group andparity to determine significance in the GLM. Sources of variation thatwere not significant were removed from the model in order to generate LSMeans. Unstimulated lymphocyte proliferation was used as a covariate inthe GLM for OVA and Con A stimulated lymphocyte proliferation since somevariability in unstimulated responses between dairy animals has beendescribed (Burton et al., 1991). Data that did not show a normaldistribution (unstimulated lymphocyte proliferation, OVA and Con Astimulated lymphocyte proliferation, and total neutrophils) as indicatedby the univariate procedure of SAS (Helwig and Council, 1982), weretransformed to natural logarithms. Lymphocyte count data was transformedusing a square root transformation. Least square means were convertedback to original units from log_(e), or square root transformed data.Consequently, standard errors of means are not shown. The Proc CORRprocedure of SAS was used to generate Pearson product moment correlationcoefficients. Results were considered to be statistically significant ifthe P-value was <0.05 and trends were reported at the p-value <0.10.

[0349] Results

[0350] Unstimulated in vitro Lymphocyte Proliferation

[0351] Individual cow, week relative to parturition, the interactionbetween antibody response group and week contributed significantly tovariation in unstimulated lymphocyte proliferation (Table 4). Herd didnot significantly affect the variation in unstimulated lymphocyteproliferative response. Unstimulated lymphocyte proliferative responsesignificantly (P<0.05) declined at parturition, but increased again atweek 3 of lactation. When these responses were evaluated by antibodyresponse group, at weeks −3, 0, 3, and 6, lymphocyte proliferativeresponses were significantly lower (P<0.01) for animals of the highantibody response group and significantly higher (P<0.05) for animals ofthe low antibody response group (FIG. 9A). The correlation betweenantibody to OVA and unstimulated lymphocyte proliferation across allgroups was negative and significant (r=−0.26, P<0.0001; Table 5).

[0352] OVA Stimulated Lymphocyte Proliferation

[0353] Individual cow and the interaction between antibody responsegroup and parity, replicate and unstimulated lymphocyte proliferation,significantly contributed to variation in OVA lymphocyte proliferativeresponse (Table 4). Herd did not significantly contribute to thevariation in lymphocyte proliferative responses to OVA. Least squaremeans of lymphocyte proliferation did not differ significantly acrossweeks. At weeks 0 and 3, lymphocyte proliferation to OVA wassignificantly lower for Group 1 (P<0.01) compared to Group 3. At week 6,the response of these groups was reversed. The correlation betweenantibody to OVA across all groups and OVA stimulated lymphocyteproliferation across all groups was negative and significant (r=−0.27,P<0.0001).

[0354] Con A Stimulated Lymphocyte Proliferation

[0355] Individual cow, parity, the interaction between parity andantibody response group, antibody response group, week and theinteraction between week and antibody response group significantlycontributed to variation in Con A stimulated lymphocyte proliferation.Herd did not significantly contribute to variation in lymphocyteproliferation to Con A. Least square means of Con A stimulatedlymphocyte proliferation declined, though not significantly, from week−4 and −3 to parturition (FIG. 9C). Proliferative responses increasedsignificantly (P<0.05) at week 3 compared to parturition. Group 1animals had the highest Con A-induced lymphocyte proliferation at weeks−4, −3, 0, 3, and 6. Response decreased in Group 1 (high response)animals from week −4 to parturition and significantly increased fromparturition to week 3. Antibody response to OVA and Con A-stimulatedlymphocyte proliferation was negatively correlated (r=−0.14, P<0.0001).

[0356] Delayed Type Hypersensitivity (DTH)

[0357] Antibody response category did not significantly affect variationin DTH response. Cutaneous DTH responses at 48 and 72 hours were highlycorrelated (r=0.90; P<0.0001). At 48 hours DTH ranged from 0 to 75% skinthickness increase with a mean of 30.7% while 72 hour values ranged from0 to 79% with a mean of 29.5%. Antibody response to OVA at week 3 didnot correlate significantly with DTH responses. The DTH response at 48and 72 hours was negatively and significantly correlated withunstimulated (r=−0.21; P<0.002; r=−0.17; P<0.01 ) and Con A (r=−0.29;P<0.0001; r=−0.28; P<0.0002) stimulated lymphocyte proliferativeresponses, respectively. DTH response at 48 hours was significantly andnegatively (r=−0.21; P<0.01) correlated with SCS.

[0358] Differential Complete Blood Cell Counts

[0359] Counts of segmented neutrophils varied between animals within allherds, but were not significantly affected by week or antibody responsegroup. Since banded neutrophils were not observed in every animal, ageneral linear model could not be used to explain the variation in thisresponse. Counts of lymphocytes declined significantly (P<0.05) fromweek −3 (4.8×10⁹ cells /mL) to week 0 (3.9×10⁹ cells/mL) (FIG. 11).Significant differences in lymphocyte numbers between antibody responsegroups were observed only at weeks 3 and 6 of lactation when Group 3animals had significantly (P<0.05) more lymphocytes compared to Groups 1and 2. Across time, only Group 3 animals had a significant decline(P<0.05) in percent and total numbers of lymphocytes from week −3 toparturition.

[0360] Milk Somatic Cell Score

[0361] For Herds 1 and 2, individual cow, week relative to parturitionand antibody response group contributed significantly to variation inresponse. In Herd 3, only the effect of cow accounted for the variationin response. Least square means of SCS in Herd 1 were lowest for animalsof the high antibody response group, and greatest in animals of the lowantibody response group at weeks 3, 4, 5 and 6 following parturition.Conversely, LS Means of SCS in Herd 2 were significantly lower foranimals of the low antibody response group compared to animals of thehigh antibody response group. Somatic cell score was negatively andsignificantly correlated with OVA stimulated lymphocyte proliferativeresponses in Herd 2 (r=−0.13; P<0.0001). Delayed type hypersensitivityat 48 hours was negatively and significantly correlated with SCS(r=−0.21; P<0.01).

[0362] Lymphocyte Subsets After Culture

[0363] Although lymphocyte subset proportions varied depending on weekrelative to parturition (week −3 to week +6), the percentage of cellspositively expressing CD2+, CD4+, CD8+, WC1+, and IgM were notsignificantly different between unstimulated control and treatmentgroups. Cells expressing IgM were most frequent (38-60%) regardless oftreatment and WC1+ cells were least numerous (5-15%) at all time points.Only at week 3 relative to parturition were there more Con A-stimulatedlymphocytes expressing IgM (60%) compared to unstimulated controls (40%)or OVA stimulated PBMC (38-40%).

[0364] Discussion

[0365] The previous example indicates that in the peripartum period,Holstein animals varied in antibody to OVA and that animals could begrouped into high, average and low groups based on this response. Theobjectives of this study were to evaluate CMI responses with respect toantibody response group and to evaluate possible associations with SCSas an indicator of udder health.

[0366] In the current study, animals with the highest antibody response(Group 1) had significantly (P≦0.05) lower unstimulated andOVA-stimulated lymphocyte proliferative responses during the peripartumperiod while low antibody response animals (Group 3) had the highestlymphocyte proliferative responses. Con A-induced lymphocyteproliferation and antibody response to OVA however, were not inverselyrelated, since animals with high antibody response also had high Con-Astimulated lymphocyte proliferative responses, indicating thatrelationships between antibody and CMI may vary depending on themeasurements made. Although no differences in DTH were observed betweenantibody response groups, DTH responses were demonstrated to varybetween individuals. This variation in DTH, a measure of CMI, indicatesthat it may be possible to select animals for enhanced CMI.

[0367] Lymphocyte counts declined in agreement with Saad et al.(1989).Group 3 animals, which that had higher unstimulated and OVA-stimulatedin vitro lymphocyte proliferation, had the sharpest decline inlymphocyte numbers at parturition. This may indicate that, althoughabsolute numbers were decreased, lymphocyte function may have beenbetter in that particular group of animals. Neutrophil counts have beenreported to decline from week −3 to parturition (Detilleux et al.,1995), however, no significant changes in neutrophil numbers wereobserved in the current study.

[0368] Previous evaluation of SCS indicated that SCS in Herd 1 waslowest for animals of the high antibody response group, and greatest inanimals of the low antibody response group at weeks 3, 4, 5 and 6following parturition (Mallard et al., 1997; Ch. I). Conversely, SCS inHerd 2 was significantly lower for animals of the low antibody responsegroup compared to animals of the high antibody response group (Ch. II).In the current study, SCS was negatively and significantly correlatedwith OVA stimulated lymphocyte proliferative responses and DTH response,indicating that sustained selection for low SCS could compromise aspectsof CMI.

[0369] Depression of lymphocyte proliferation during the postpartumperiod has been demonstrated previously in humans (Weinberg, 1984),sheep (Burrels et al., 1978), and dairy cattle (Wells et al., 1977;Manak et al., 1982; Kashiwazaki et al., 1985; Ishikawa, 1987; and Kehrliet al., 1989a). Ishikawa (1987) demonstrated decreased blastogenicresponse in PBMC stimulated with Con A and pokeweed mitogen (PWM) fromthe third trimester of pregnancy, which reached a minimum atparturition. Saad et al. (1989) described a depressed Con A-,phytohemagglutinin (PHA)-, and PWM-stimulated lymphocyte proliferationthat started only 1 week prior to parturition and was minimal one daybefore parturition. Saad et al. (1989) also evaluated milk mononuclearcell (MC) proliferative responses and, in contrast to PBMC, milk MC didnot increase in proliferative response two weeks after lactation.Peripartum depression of lymphocyte proliferation was observed inunstimulated lymphocyte proliferative responses between weeks −3 andparturition (week 0), and a depression of response to Con A was alsoobserved. The largest (P<0.05) depression of Con A stimulated lymphocyteproliferative responses at parturition were observed in animals with ahigh antibody response phenotype (Group 1). Again, this may indicate anegative association between high antibody response and certainindicators of CMI in dairy animals, which would need to be considered inthe development of a selection index for high and low immune response.

Example 4

[0370] The Relationship Between Milk Production and Antibody Response toOvalbumin (OVA) During the Peripartum Period

[0371] Suboptimal innate and immune mechanisms of host resistance duringthe peripartum period may contribute to increased incidence of mastitis.To evaluate associations between antibody response to OVA and milkproduction variables during the peripartum period, 136 Holstein cows andheifers from 3 herds with known antibody response profiles, wereevaluated for projected 305-day milk, protein, and fat yield. Using amathematical index, animals were quantitatively classified based ontheir antibody responses to OVA into high (Group 1), average (Group 2)or low (Group 3) response groups. Group 3 had the highest (P<0.0001)milk yield (8448.6 kg) compared to Groups 1 (8191.2 kg) and 2 (8174.8kg). Group 3 had the highest 305-day predicted protein (279.8 kg) andfat yield (343.1 kg) compared to Groups 1 (263.5 kg, 314.0 kg) and 2(261.4 kg, 314.9 kg) respectively. However, in two out of the threeherds investigated, Group 1 animals had no incidence of clinicalmastitis compared to other antibody response groups. Although thissuggests that animals with low antibody response produce more milk, fatand protein, and therefore more income, mastitis occurrence was observedto be highest for these animals in two out of three herds investigated.The development of animals that produce optimal levels of milk withreduced occurrence of mastitis may be possible through selectivebreeding for both production and enhanced immune response.

[0372] The objective of this example was to evaluate the effect ofantibody response group on 305-day projected production traits (milk,fat, and protein) and relate production and immune response associationswith disease occurrence.

[0373] Materials and Methods

[0374] Animals and Treatments

[0375] Phenotypic variation in immune responses of 136 Holstein cows andheifers from 2 research herds (n=32; n=67) and 1 commercial herd (n=37)were examined from week −3 relative to calving (week 0) to six weekspostpartum (week 6). Eighty-eight animals were multiparous cows and 48were primiparous heifers. As described previously (Mallard et al., 1997;Ch. V), to stimulate antibody response during the peripartum period,animals received an intramuscular (im) injection of ovalbumin antigen(OVA , Type VII, Sigma Chemical Co., St. Louis, Mo.) and a mastitisendotoxemia preventive vaccine, an Rc mutant of Escherichia coli O111:B4(Rhône Mérieux Escherichia coli J5, Rhône Mérieux, Lenexa, Kans.)approximately 8 weeks (4 mg OVA) and 3 weeks (2 mg OVA) prior topredicted calving dates. At parturition (week 0), animals received asingle immunization of the OVA dissolved in phosphate buffered saline(PBS-0.1 M, pH 7.4) (2 mg, im). Using a mathematical model describedpreviously (Ch. II), animals were categorized based on their antibodyresponse to OVA and grouped into high (Group 1), average (Group 2) andlow (Group 3) antibody response phenotypes.

[0376] Production Variables

[0377] Projected 305 day milk, fat, and protein yields were obtainedfrom the Ontario Dairy Herd Improvement Corporation (Ontario DHI). Thelast test day before the end of lactation was used to calculateprojected 305-day milk, fat and protein and was based on at least 100days in milk (DIM).

[0378] Statistical Methods

[0379] Type III least squares analysis of variance (ANOVA) and correctedmeans (least square means, LS Means) were generated using the GeneralLinear Models (GLM) Procedure of the Statistical Analysis System (SAS;Helwig and Council, 1982) to evaluate the effects of herd, season-year,antibody response group, parity, week, and their interactions milk, fat,and protein yield (Table 6). Results were considered to be statisticallysignificant if the p-value was <0.05 and trends were reported at thep-value <0.10.

[0380] Results

[0381] Effects of Antibody Response Group on Milk Production VariablesMilk Yield

[0382] Parity and the interaction between antibody response group andparity contributed significantly (P≦0.0001) and antibody response grouptended (P≦0.06) tended to contribute to variation in projected 305-daymilk yield (Table 6). Group 3 animals had a significantly higher(P<0.0001) 305-day cumulative milk yield (8448.6 kg) compared to average(8174.8 kg) and high (8191.2 kg) antibody responding dairy animals (FIG.12A).

[0383] Protein

[0384] Antibody response group, parity and the interaction betweenantibody response group and parity contributed significantly (P<0.0001)to variation in protein yield (Table 6). Group 3 animals had asignificantly higher (P<0.0001) 305-day protein yield (279.8 kg)compared to average (261.3 kg) and high (263.5 kg) antibody responderanimals (FIG. 12B).

[0385] Fat

[0386] Antibody response group, parity, and the interaction betweenantibody response group and parity significantly (P<0.0001) contributedto variation in 305-day fat yield (Table 6). Group 3 animals had asignificantly higher (P<0.0001) 305-day cumulative fat yield (343.1 kg)compared to average (314.9 kg) and high (314.0 kg) antibody respondinganimals (FIG. 12C).

[0387] Discussion

[0388] The current study suggests that animals with the highest antibodyresponse have lower milk, fat and protein yield. However, animals thathave high antibody response in two out of the three herds evaluated werereported (Ch.II) to have the lowest occurrence of mastitis compared toanimals with low antibody response. Given the positive correlationbetween the selection for increased milk production and the increasedrate of clinical mastitis occurrence, one might hypothesize thatsuperior production could be associated with unfavourable changes inhost defense which could result in a higher occurrence of mastitis. Thefact that animals of average and high antibody response (Groups 1 and 2)tended to produce less milk and milk solids per lactation than animalsof the low antibody response group might indicate that selection basedon antibody response to OVA is not economically feasible in the shortterm. At a price of $5.15/kg of fat and $8.39/kg protein (Ontario MilkProducer, October 1997), animals with low antibody response would earnan estimated revenue of Cdn$ 4114.49/lactation (based on fat and proteincomponent pricing only) followed by animals with high antibody responseat $3827.87 per lactation ($286.62 less than Group 3 animals), andanimals with average antibody response at $3814.04 per lactation($300.45 less than low antibody response animals and $13.82 less thanhigh response animals). In the long term however, it may be morebeneficial to own animals with superior health traits that minimizedisease-related costs (approximately $140-300/cow/lactation in Ontario;Zhang et al., 1993) and still produce milk at an optimal level ofproduction quantity and quality. A previous U.S. study (Dunklee et al.,1994) determined that health costs were positively associated withhigher production, however, health costs did not outweigh profitpotential. Regardless of whether health costs do or do not have animpact on the production profit potential of dairy animals, reducedoccurrence of mastitis will nonetheless be mutually beneficial to dairyproducers, processors and consumers. Milk producers will benefit througha reduction in economic loss incurred by mastitis, processorsmanufacturing milk products will benefit from an enhancement in milkquality, and consumers concerned about animal welfare and food safetystandards will appreciate knowing that antibiotic usage to treatmastitis has been reduced as a direct result of reduced mastitisoccurrence. Further, as disclosed in this description, as certain immuneresponse traits are heritable, it would be possible to select or breedcows with a desired level of immune response which should influencedisease resistance. Milk quantity and quality may also be influenced bysuch breeding practice. It may be that cows with higher than averagedisease resistance and with high, but not maximum milk yields wouldresult in maximum profits.

Example 5 Effects of Growth Hormone, Insulin-like Growth Factor-I, andCortisol on Periparturient Antibody Response Profiles of Dairy Cattle

[0389] The objectives of this example were to determine hormone andantibody response profiles from the prepartum period to peak lactation,and evaluate potential immunomodulatory effects of the classic endocrinehormones, growth hormone (GH), insulin-like growth factor-I (IGF-I) andcortisol. Specifically, 33 Holstein cows were immunized with ovalbumin(OVA) and Escherichia coli J5 at weeks −8 and -3 prior to parturition.At parturition (week 0), cows received an additional immunization ofOVA. Blood was collected at weeks −8, −3, 0, 3 and 6 relative toparturition and various samples were used to determine plasma hormoneconcentration, serum immunoglobulin (Ig), and specific antibody responseto OVA and E. coli. Colostrum and milk samples were also collectedpost-parturition to monitor local immunoglobulin and antibody responses.Results indicated that not all periparturient cows exhibited depressedimmune response, and that antibody response to OVA could be used topartition cows into 3 groups recognizing animals with sustainedmeasurable antibody response before and after parturition (Group 1),animals which responded poorly to immunization at parturition (Group 2),and animals which did not respond to immunizations at week −3 orparturition (Group 3). Cows with the highest antibody response to OVA(Group 1) also tended (P≦0.10) to have the highest response to E. coliJ5 at parturition and had the lowest incidence of disease, particularlymastitis. Antibody response to OVA measured in milk tended to be higherin Group 1 cows, particularly at week 0 (P≦0.06) compared to cows ofGroup 3. IGF-I was higher (P≦0.05) in cows of Group 1 than Group 3 atpeak lactation (week 6).

[0390] To further understand the complex endocrine-immune interactionsthat occur around parturition and their impact on host resistance, weutilized the dairy cow as a large animal stress model of pregnancy,parturition, and lactation. To evaluate peripartum and peak lactationimmune response and hormone profiles, 33 cows were immunized withovalbumin (OVA) and Escherichia coli (E. coli ) J5. Blood samples werecollected to measure antibody response, GH, IGF-I, and cortisolconcentrations at dry-off (approximately 8 weeks prepartum) and weeklyfrom week −3 to week 6 postpartum.

[0391] Materials and Methods

[0392] Animals and Treatments

[0393] Antibody response and hormone profiles of 33 Holstein cows wereexamined from approximately eight weeks prepartum (week −8) based onpredicted calving dates to six weeks postpartum (week 6). Twenty-sixanimals were multiparous cows and seven were primiparous heifers. Todetermine associations between periparturient immune responses andhormone profiles, animals received an intramuscular (im) injection of amastitis endotoxemia preventive vaccine with the manufacturer's adjuvant(Rhône Mérieux E. coli J5, Rhône Mérieux, Lenexa, Kans.) along with theantigen, OVA (Type VII, Sigma Chemical Co., St. Louis MO), at weeks −8(4 mg) and −3 (2 mg). At parturition (week 0), cows received anadditional immunization of OVA without adjuvant dissolved in phosphatebuffered saline (PBS—0.1 M, pH 7.4) (2 mg, im). OVA was chosen as aninert antigen to which these animals had not been previously exposed. E.coli J5 was used as an antigen previously recognized by most dairy cowsand of more complex response, but of biological relevance. Animals wereinitially classified according to their serum antibody response curvekinetics to OVA as either high responders (Group 1) relative to cowsthat exhibited a lack of measurable response to immunization eitherpostpartum (Group 2) or pre- and postpartum (Group 3) (FIG. 13A).

[0394] Blood and Milk Sampling Schedule

[0395] Peripheral blood was collected via tail venipuncture at week −8,and weekly from weeks −3 to 6 relative to parturition. Various sampleswere used to monitor plasma hormone concentrations (GH, IGF-I,cortisol), serum immunoglobulin G_(1 & 2), and specific antibodyresponse to OVA and E. coli J5. Colostrum and milk samples werecollected to monitor specific antibody to OVA and to monitor total IgG₁(weeks 0, 3, 6) and IgG₂ (weeks 0 and 3). Colostrum was collected at thefirst milking following parturition. Milk samples were stripped from allquarters approximately 2-4 hr after morning milking. Colostrum and milksamples were stored frozen without preservative at −20° C. until time ofwhey separation and immunoglobulin quantification.

[0396] ELISA for OVA Antibody Detection In Serum and Whey

[0397] Serum was separated from coagulated peripheral blood bycentrifugation and stored frozen (−20° C.) until time of assay. Milksamples were stored frozen (−20° C.) until time of assay when they werecentrifuged twice (11,000 g, 15 min) to separate fat from whey. Antibodyto OVA was detected by ELISA and quantified based on optical densitymeasurements according to the procedure described by Burton, et al.1993. Briefly, 96-well polystyrene plates (Fisher Scientific, Don Mills,Ont.) were coated with a 3.11×10⁻⁵ M solution of OVA (OVA, Type VII,Sigma Chemical Co., St. Louis Mo.) dissolved in carbonate-bicarbonatecoating buffer (pH 9.6). Plates were incubated (4° C., 48 h), thenwashed with PBS and 0.05% Tween 20 solution, (pH 7.4). Plates wereblocked with a PBS-3% Tween 20 solution and incubated (room temperature;rt, 1 h). Plates were washed and diluted test sera (1/50 and 1/200) ormilk whey (Neat, 1/10, 1/100 and 1/400) and controls were added using aquadrant system (Wright, 1987). Sera samples were added in duplicate,and whey samples were added in quadruplicate. Negative and positivecontrols included a pooled sample of pre-immunization sera and a pooledsample of sera from cows 14 days post secondary immunizationrespectively. Plates were incubated at rt for 2 h. Subsequently,alkaline phosphatase conjugate rabbit anti-bovine IgG (whole molecule)(Sigma Chemical Co., St. Louis, Mo.) was dissolved in wash buffer, addedto the plates and incubated (rt, 2 h). P-Nitrophenyl Phosphate Disodiumtablets (pNPP) (Sigma, St. Louis, Mo.) were dissolved in a 10%diethanolamine substrate buffer, (pH 9.8). Plates were washed with washbuffer, pNPP was added to the plates and was then incubated at rt for 30minutes (min). Plates were read on a EL311 automatic ELISA plate reader(BIO-TEK Instruments, Highland Park, Vt.) and the optical density (OD)was recorded at 405 and 630 nanometres (nm) when the positive controlreached OD≧0.999. The mean of the number of replicates added to eachplate was corrected to an OD=1.0 by multiplying by the inverse of themean of the positive controls. Corrected means of each dilution werethen added together to give an additive OD value, indicative of antibodyresponse.

[0398] ELISA for E. coli J5 Antibody Detection In Serum

[0399] According to the method described by Rhône-Mérieux Animal Health(Lenexa, Kans.; 1994 personal communication), heat-killed E. coli strainJ5 (ATCC, Rockville, Md.) was coated at a concentration of 6.25×10⁷colony forming units per mL onto Dynatech Immulon II polystyrene 96-wellflat bottom plates overnight at 4° C. After washing with wash buffer(PBS plus 0.05% Tween 20), 1% gelatin was added to block non-specificbinding and plates were incubated (rt, 1 h). Plates were washed and fourreplicates of test serum (dilutions of 1/1000, 1/1500, 1/2000 and1/2500) were added using a modified quadrant system. PBS−0.05% Tween 20was used as a blank and fetal calf serum (FCS, Bockneck Laboratories,Can Sera, Rexdale, Ont.) was used as a negative control. Negative andpositive controls prepared from pooled pre- and post immunization serawere plated respectively. Test sera were incubated (rt, 2 h), and plateswere washed with PBS−0.05% Tween 20. Horseradish peroxidase conjugategoat anti-bovine IgG whole molecule in PBS (1/4000; The Binding Site,Birmingham, UK) was added and the plates were incubated (rt, 1 h). Afterwashing, the substrate, 2,2′-azino-di-(3-ethyl-benzthiazolinesulphonate-6) (ABTS; Boehringer Mannheim, Laval, Que.) was added andplates were incubated (rt, 30 min). Plates were then read on an EL311automatic ELISA plate reader (BIO-TEK Instruments, Highland Park, Vt.)and OD recorded at 405 nm and 490 nm. The mean OD of the four samplereplicates were corrected to an OD=1.0. Based on the immunizationprotocol and phenotypic observation of antibody response curve kineticsof all dilutions tested, the 1/1000 dilution consistently allowed fordifferentiation between positive and negative controls, exhibitingminimal prozone effect and therefore was the dilution of choice forcomparison between animals.

[0400] Radial Immunodiffusion Assay

[0401] Radial immunodiffusion was used according to a method previouslydescribed (Mallard et al. 1992) to determine the concentrations ofIgG_(1&2) in serum and whey from colostrum and milk. Whey from weeks 0and 3 were tested for the IgG_(1&2) subclasses. At week 6 however, IgG₁only was tested in whey since very low concentrations of IgG₂ exist innormal milk (Butler, 1980).

[0402] Disease Occurrence

[0403] Occurrence of infectious and metabolic diseases were recordedthroughout the study period since connections within theendocrine-immune axis may conceivably affect both. Disease events wereclassified by number as follows: none=0, mastitis=1, ketosis=2, andother (diseases occurring at lower frequency in this study; for example,milk fever and pneumonia)=3.

[0404] Somatic Cell Count

[0405] Milk (AM/PM composite sample) was collected weekly during milkingto determine somatic cell count (SCC). Only the SCC counts whichcoincided with the day of blood sample collection for each week arereported. SCC, an indicator of subclinical mammary gland infection, wastransformed to somatic cell score (SCS) for analysis. SCS is the naturallogarithm of SCC in cells/μL and is calculated as follows (Shook, 1993):

SCS=log_(e)(SCC/100)÷log_(e)(2)+3

[0406] Hormone Assays

[0407] Peripheral blood samples collected for hormone assay wereimmediately put on ice. Samples were centrifuged at 4° C. and the plasmawas removed. Plasma from each cow was individually aliquoted intomultiple 1 mL containers and stored frozen at −20° C. until time of eachhormone assay.

[0408] Cortisol

[0409] Plasma cortisol concentration (μg/dL) was determined using acommercially available Gamma Coat Cortisol ¹²⁵I, RIA kit (INCSTARCorporation, Stillwater, Minn.). The assay sensitivity was 0.21 μg/dLand the inter- and intra-assay CV were less than 10%.

[0410] IGF-I and GH

[0411] Radioimmunoassay (RIA), as described previously by Elsasser etal. (1989), was used to determine the IGF-I concentration (ng/mL) ofsamples. The IGF-I used for tracer and standards was recombinantthreonine-59-substituted human IGF-I (Amgen; Thousand Oaks, Calif.).Based on duplicate samples, all performed on the same day, theintra-assay CV was less than 10%. GH concentration (ng/mL) wasquantified using RIA (Elsasser et al., 1988).

[0412] Statistical Methods

[0413] Least squares analysis of variance (ANOVA) and corrected means(least square means, LS Means) were generated using the General LinearModels (GLM) Procedure of the Statistical Analysis System (SAS; Helwigand Council, 1979). The statistical models used in this study includedfixed effects of antibody response groups (1, 2, 3), cow nested withinantibody response group, and week relative to parturition (weeks −3, 0,3, and 6). In preliminary analysis, the effect of parity was notsignificant and was therefore removed from all subsequent models. Amodel was constructed for the following dependent variables: antibodyresponse to OVA in sera and whey, antibody response to E. coli J5 insera, and the concentration of IgG_(1&2) in serum and whey. Sources ofvariation included in the model for each dependent variable aresummarized in Table 7. Hormone concentrations (GH, IGF-I, cortisol) wereincluded as covariates in all models. Data that did not show a normaldistribution as indicated by the univariate procedure of SAS, weretransformed to natural logarithms. Pearson product moment correlationcoefficients between immune response variables and hormoneconcentrations were generated using the correlations procedure of SAS(Proc CORR). Results were considered to be statistically significant ifthe P-value was ≦0.05 and trends were reported at the P-value ≦0.10.

[0414] Results

[0415] Antibody Response to OVA

[0416] Serum

[0417] Serum antibody response to OVA varied significantly over theperipartum period and individuals could be readily classified into threeimmune response groups: high responders (Group 1, n=12; 6 heifers, 6cows) relative to animals which exhibited a lack of measurable responseto immunization either postpartum (Group 2, n=12 cows) or pre- andpostpartum (Group 3, n=9; 8 cows, 1 heifer). Approximately 1/3 (Group 1)of the animals showed consistent, above average serum antibody responseto OVA following immunization at weeks −8, −3, and 0 relative toparturition. The remaining animals had either an average amount ofantibody, or had responses lower than the population mean and did notrespond following immunization at week −3 or 0 relative to parturition(FIG. 13A). All cows including those of Group 3 exhibited responsesgreater than background (week −8) at week −3 and therefore wereconsidered low responders rather than non-responders. ANOVA indicatedthat the statistical model accounted for 94.19% of the total variationin serum antibody response to OVA over the peripartum period, and thatthe effects of cow (P≦0.0001), antibody response group (P≦0.005), andthe interaction between antibody response group and week (P≦0.0001),contributed significantly to the variation in antibody response to OVA(Table 7). Growth hormone (GH) exhibited some tendency to be positivelyassociated with antibody response to OVA (P≦0.15). Animals in Group 1,with the highest antibody response to OVA, consistently had the highestGH concentrations in plasma at each sample week in comparison to animalsin Groups 2 and 3 (FIG. 14A). Although these differences as determinedin the ANOVA may not have been statistically significant (Table 7),correlation analysis indicated a significant and positive relationship(r²=0.29, P≦0.001) between antibody response to OVA and GH, regardlessof week or antibody response group (Table 8). This would suggest thatthere is biological significance to the consistently higher GHconcentrations in the high immune response group (J. L. Burton 1991, PhDThesis, University of Guelph). LS Means of IGF-I and cortisolconcentrations in plasma (FIGS. 14B,C) were not significantly differentbetween immune response groups, except at week 6 when Group 1 cows hadhigher concentrations of IGF-I (P≦0.05) compared to cows in Group 3(FIG. 14B). Correlation analysis of antibody response to OVA indicatedrelationships with IGF-I (r²=−0.19, P≦0.04) and cortisol (r²=0.17,P≦0.06) (Table 8).

[0418] Whey

[0419] ANOVA indicated that cow (P≦0.006), antibody response group(P≦0.003) and the interaction between IGF-I concentration and week(P≦0.005) contributed significantly to the variation in whey antibodyresponse (Table 7). There was a tendency for week relative toparturition (P≦0.06) to associate with antibody response to OVA in whey.Corrected population least square means (LS Means) of antibody responseto OVA in whey declined significantly following parturition, such thatat week 0 the OD value was 1.68±0.17 compared to 0.85±0.17 (P≦0.004) atweek 3 and 0.50±0.20 (P≦0.0001) at week 6 (FIG. 13B). At parturition,there was a tendency (P≦0.06) for antibody response to OVA in whey todiffer between Groups 1 (1.96±0.26) and 3 (1.33±0.23). Comparable to theantibody response to OVA in serum, correlation analysis indicated asignificant relationship between OVA antibody response in whey with GH(r²=0.31, P≦0.0005) and IGF-I (r²=−0.22, P≦0.01) (Table 8).

[0420] Antibody Response to E. coli J5

[0421] Only the effect of cow (P<0.0002) contributed significantly tothe variation in antibody response to E. coli J5 . Pre-immunization sera(week −8) indicated that these cows had minimal background OD values ofmeasurable E. coli J5 specific antibody prior to vaccination (populationmean±SEM=0.314±0.11; n=33) compared to post-vaccination naturalantilogarithm OD values at week −3 (0.663) and week 0 (0.830). Antibodyresponse to E. coli when grouped by antibody response group (1, 2, or3), indicated that only at parturition (week 0) did Group 1 animals tend(P≦0.10) to have a higher concentration of E. coli specific antibody (ODvalue=1.053) than Group 3 animals (OD value=0.702). Correlation analysisindicated that GH was significantly correlated with antibody response toJ5 E. coli (r²=0.18, P≦0.04) (Table 8). Antibody response to E. coli J5was positively correlated with antibody response to OVA (r²=0.59,P≦0.0001).

[0422] IgG₁ & IgG₂ in Serum, Colostrum, and Milk

[0423] Antibody response group significantly contributed to thevariation of serum IgG₂ (P≦0.002) only. There was a tendency for theinteraction between IGF-I and week relative to parturition to accountfor variation in total whey IgG₁ concentration (P≦0.07). The modelconstructed for whey IgG₂ was unable to explain the variation in thisresponse. Correlation analysis indicated a significant, negativerelationship between GH and IgG₁ in serum (r²=−0.26; P≦0.01).Conversely, IGF-I tended to correlate positively with total IgG₁ inserum (r²=0.19, P≦0.07). Growth hormone (r²=0.26, P≦0.03) and IGF-I(r²=−0.20, P≦0.10) correlations with IgG₁ in whey were reversed fromthat in serum.

[0424] Disease Occurrence

[0425] Records of disease events indicated that 54.5% of the 33 animalsevaluated were considered healthy during this study. Of the diseasedanimals, 7 cows had mastitis events (21.21%), 7 had ketosis events(21.21%) and 3 cows had other disease events (9.09%) while on thisstudy. Group 1 animals which showed a consistent above average antibodyresponse to OVA, had the lowest percent occurrence of disease (FIG. 15)and actually had no occurrence of clinical mastitis.

[0426] Somatic Cell Score (SCS)

[0427] At parturition, LS Means of SCS were significantly lower (P≦0.05)for Group 2 cows (SCS=3.2) compared to Group 1 (SCS=4.36) and Group 3(SCS=4.98) cows. At weeks 2, 3, 4, and 6 after parturition, all groupsdiffered significantly from one another, and, Group 1 cows consistentlyhad the lowest SCS while Group 3 cows consistently had the highest SCS.

[0428] Hormones

[0429] At parturition, least square mean (LS Mean) concentrations of GH(FIG. 14A) and cortisol (FIG. 14C) were at a maximum while IGF-I (FIG.14B) was at a minimum. After parturition, GH concentrations decreased(P≦0.05) until week 6. Cortisol concentrations also decreased (P≦0.05)post-parturition and then increased slightly after week 3. In contrast,IGF-I concentrations decreased (P≦0.05) at parturition and thencontinued to increase (P≦0.05) toward peak lactation.

[0430] The present study is the first to simultaneously evaluatespecific antibody responses and hormone profiles during the peripartumperiod and has revealed some associations between IGF-1 and antibodyresponse, however, no actual cause and effect relationship can beestablished from this study. In addition, an elevation of plasma IGF-Iduring the latter stages of pregnancy followed by a dramatic declinearound parturition with a steady increase in concentrations followingparturition was demonstrated. Some of these observations have beenconfirmed in the literature; for instance, Vega et al. (1991) attributedchanges in IGF-1 and GH to the decrease in metabolic demands associatedwith the cessation of milk production, during late gestation, followedby an increase in metabolic demand associated with the onset oflactation at parturition. As well, the demand of the mammary gland mayalter the transport of IGF-I by sequestering it from the blood.Lactogenic hormones, such as prolactin and cortisol, may also preventthe synthesis of IGF-I and IGF-I binding proteins (Vega et al., 1991).

[0431] As previously reported (Hoshino et al., 1991), circulatingconcentrations of GH increased around parturition, concurrent with earlymilk production, and decreased as lactation progressed. The inverserelationship between peripartum IGF-I and GH is noteworthy in that IGF-Iproduction is normally dependent on GH as blood concentrations influenceliver production of IGF-I (Burton et al., 1992). However, due to theperipartum uncoupling between these two hormones, it may be possible toevaluate the influence of each hormone separately on both the innate andhumoral aspects of the immune system.

[0432] Although the interaction of GH, and to a lesser extent IGF-I,with the immune system has been widely reported in a variety of speciesincluding dogs, humans and mice, direct effects of GH on lymphoid cellshave not been unequivocally demonstrated. For example, GH deficientpatients often are not found to be immuno-compromised (Fornari et al.,1994). Furthermore, various studies have demonstrated that the immunesystems of GH deficient children treated with GH can be normal,suppressed or even enhanced (Gupta et al., 1983; Kelley, 1990; Petersenet al., 1990). It is also suggested that some effects of GH on theimmune system are a result of IGF-I (Burton et al., 1992; Badolato etal., 1994). In general these studies indicate that the preciserelationships between these hormones and immune responsiveness will bechallenging to untangle. In the present study, GH concentration waspositively, and IGF-I negatively correlated with antibody response.Animals in Group 1 with the highest antibody response to OVA, tended tohave the highest GH concentrations. Although some of the results werenot necessarily statistically significant, correlation analysisindicated a positive relationship between antibody and GH. For thisreason the present invention may be used to select high immuneresponders with naturally enhanced levels of growth hormone. Thus thebenefits of higher levels of growth hormone in animals could beobtained, while avoiding the side effects associated with artificiallyenhancement of growth hormone levels such as those associated with theuse of synthetic growth hormones. For the most part, IGF-Iconcentrations were not different among immune response groups, exceptat week 6 when cows of Group 1 had significantly higher concentrationsthan Group 3 cows. Thus, the methods of the present invention could beused to identify or select for animals with high post-peripartim IGF-1levels. These results are consistent with the work of Yoshida et al.,(1992) which demonstrated that GH stimulates B cell growth and Igsynthesis by B cells and B cell lines. Growth hormone has been reportedto alter antibody synthesis in response to T-dependent antigens, as wellas increase activity of T lymphocytes and natural killer (NK) cells(Geffner et al., 1990; Schurmann et al., 1995). Badolato et al., (1994)found that B cells displayed relatively high numbers of GH receptors,whereas T and NK cells showed much lower numbers of receptors. Inaddition, increased GH concentrations can enhance otherwise suppressedantibody response due to stress released glucocorticoids (Franco et al.,1990). Again, it has been suggested that the effects of GH may bemediated through IGF-I, a lymphocyte growth factor (Franco et al.,1990), but whether this is true during the peripartum period when thesehormones become uncoupled seems unlikely.

[0433] While the present invention has been described with reference towhat are presently considered to be the preferred examples, it is to beunderstood that the invention is not limited to the disclosed examples.To the contrary, the invention is intended to cover variousmodifications and equivalent arrangements included within the spirit andscope of the appended claims.

[0434] All publications, patents and patent applications are hereinincorporated by reference in their entirety to the same extent as ifeach individual publication, patent or patent application wasspecifically and individually indicated to be incorporated by referencein its entirety.

FULL CITATIONS FOR REFERENCES REFERRED TO IN THE SPECIFICATION

[0435] AArestrup, F. M., Jensen, N. E., and Østergård, H., 1995.Analysis of associations between major histocompatibility complex (BoLA)class I haplotypes and subclinical mastitis of dairy cows. J. DairySci., 78:1684-1692.

[0436] Arthur, R. P., and Mason, D., 1986. T cells that help B cellresponses to soluble antigen are distinguishable from those producinginterleukin 2 on mitogenic or allogenic stimulation. J. Exp. Med.,163:774-786.

[0437] Bachen, E. A., Manuck, S. B., Marsland, A. L., Cohen, S.,Malkoff, S. B., Muldoon, M. F., and Rabin, B. S., 1992. Lymphocytesubset and cellular immune responses to a brief experimental stressor.Psychosom. Med., 54:673-679.

[0438] Badolato, R., Bond, H. M., Valerio, G., Petrella, A., Morrone,G., Waters, M. J., Venuta, S., and Tenore, A., 1994. Differentialexpression of surface membrane growth hormone receptor on humanperipheral blood lymphocytes detected by dual fluorochrome flowcytometry. J. Clin. Endocrinol. Metab., 79:984-990.

[0439] Banos, G., and Shook, G. E., 1990. Genotype by environmentinteraction and genetic correlations among parities for somatic cellcount and milk yield. J. Dairy Sci., 73:2563-2573.

[0440] Bauman, D. E., Eppard, P. J., DeGeeter, M. J. and Lanza, G. M.,1985. Responses of high-producing dairy cows to long-term treatment withpituitary somatotropin and recombinant somatotropin. J. Dairy Sci.,68:1352-1362.

[0441] Biozzi, G., Stiffel, C., Mouton, D., Bouthillier, Y., andDeereusefound, C., 1968. A kinetic study of antibody producing cells inthe spleen of mice immunized intravenously with sheep erythrocytes.Immunol., 14:7-20.

[0442] Biozzi, G., Stiffel, C., Mouton, D., Bouthillier, Y., andDecreusefond, C., 1972. Cytodynamics of the immune response in two linesof mice genetically selected for high and low antibody synthesis. J.Exp. Med., 135:1071-1094.

[0443] Biozzi, G., Mouton, D., San{acute over (t)} Anna, O. A., Passos,H. C., Gennari, M., Bouthillier, Y. Ibanez, O. M., Stiffer, C., andSiquerira, M., 1979. Genetics of immunoresponsiveness to naturalantigens in the mouse. Curr. Top. Microbiol. Immunol., 85:31-98.

[0444] Blalock, J. E., 1994. The syntax of immune-neuroendocrinecommunication. Immunol.Today, 15:504-511.

[0445] Boettcher, P. J., Hansen, L. B., Van Raden, P. M. and Ernst, C.A., 1992. Genetic evaluation of Holstein bulls for somatic cells in milkof daughters. J. Dairy Sci., 75:1127-1137.

[0446] Bruecker, K. A. and Schwartz, L. W., 1982. Bovine peripheralblood polymorphnuclear neutrophil chemotactic response to Pastuerellahemolytica or zymosan-activated serum. Am. J. Vet. Res., 43:1879-1881.

[0447] Burrells, C., Wells, P. W., and Sutherland, A. D., 1978.Reactivity of ovine lymphocytes to phytohemagglutinin and pokeweedmitogen during pregnancy and in the immediate post-parturient period.Clin. Exp. Immunol., 55:410-415.

[0448] Burton, J. L., Mallard, B. A., and Mowat, D. N., 1993. Effects ofsupplemental chromium on immune resposes of periparturient and earlylactation dairy cows. J. Anim. Sci., 71(6):1532-1539.

[0449] Burton, J. L., Burnside, E. B., Kennedy, B. W., Wilkie, B. N.,and Burton, J. H., 1989a. Antibody responses to human erythrocytes andovalbumin as marker traits of disease resistance in dairy calves. J.Dairy Sci., 72:1252-1265.

[0450] Burton, J. L., Kennedy, B. W., Burnside, E. B., Wilkie, B. N.,and Burton, J. H., 1989b. Dinitrochlorobenzene contact hypersensitivityas a marker trait for selection to improve disease resistance in calves.J. Dairy Sci., 72:2351-2361.

[0451] Burton, J. L., McBride, B. W., Kennedy, B. W., Burton, J. H.,Elsasser, T. H. and Woodward, B., 1991. Influence of exogenous bovinesomatotropin on the responsiveness of peripheral blood lymphocyte tomitogen. J. Dairy Sci., 74:916-928.

[0452] Burton, J. L., McBride, B. W., Kennedy, B. W., Burton, J. H.,Elsasser, T. H., and Woodward, B., 1992. Contact sensitivity andsystemic antibody responses in dairy cows treated with recombinantsomatotropin. J. Dairy Sci., 75:747-755.

[0453] Burton, J. L., Nonnecke, B. J., Elsasser, T. H., Mallard, B. A.,Yang, W. Z., and Mowat, D. N., 1995. Immunomodulatory activity of bloodserum from chromium-supplemented periparturient dairy cows. Vet.Immunol. Immunopath., 49:29-38.

[0454] Burvenich, C., Paape, M. J., Hill, A. W., Guidry, A. J., Miller,R. H., Heyneman, R., Kremer, W. D. J. and Brand A., 1994. Role of theneutrophil leukocyte in the local and systemic reactions duringexperimentally induced E.coli mastitis in cows immediately aftercalving. Vet. Quart., 16:45-50.

[0455] Butler, J. E., 1980. A concept of humoral immunity amongruminants and an approach to its investigations. The Ruminant ImmuneSystem: Proc. Int. Symp. Rum.Imm. Sys., Plymouth, N.H., pp. 3-55.

[0456] Butler, W. R., Everett, R. W., and Coppock, C. E., 1981. Therelationships between energy balance, milk production, and ovulations inpostpartum Holstein cows. J. Anim. Sci., 53:742-748.

[0457] Byfield, P. E., and Howard, J. G., 1972. Equivalentgraft-versus-host reactivity of spleen cells from two lines of micegenetically selected for high and low humoral antibody formation.Transplant. 14:133-134.

[0458] Cai, T., Weston, P. G., Lund, L. A., Brodie, B., McKenna, D. J.,Wagner, W. C., 1994. Association between neutrophil functions andperiparturient disorders in cows. Am. J. Vet. Res., 55:934-943.

[0459] Caroll, E. J., Mueller, R. and Panico, L., 1982. Chemotacticfactors for bovine leukocytes. Am. J. Vet. Res., 43:1661-1664.

[0460] Caroll, E. J., 1983. Immunological aspects of coliform mastitis.Vet. Res. Comm. 7:247.

[0461] Chang, X., Mallard, B. A., and Mowat, D. N., 1994. Proliferationof peripheral blood lymphocytes of feeder calves in response tochromium. Nut. Res., 14:851-864.

[0462] Clarke, A. G. and Kendall, M. D., 1994. The thymus in pregnancy:the interplay of neural, endocrine and immune influences. Immunol.Today, 15(11): 545-551.

[0463] Colditz, I. G., and Maas, P. J. C., 1987. The inflammatoryactivity of activated complement in ovine and bovine mammary glands.Immunol. Cell Biol., 65:433436.

[0464] Craven, N., 1986. Chemotactic factors for bovine neutrophil inrelation to mastitis. Comp. Immunol. Microbiol. Inf. Dis., 9:29-36.

[0465] Craven, N., and Williams, M. R., 1985. Defences of the bovinemammary gland against infection and prospects for their enhancement.Vet. Immunol. Immunopath. 10:71.

[0466] Cullor, J., 1991. The Escherichia coli J5 vaccine: Investigatinga new tool to combat coliform mastitis. Vet. Med., 836-844.

[0467] Daley, M. J., Oldham, E. R., Williams, T. J., and Coyle, P. A.,1991. Quantitative and qualitative properties of host polymorphnuclearcells during experimentally induced Staphylococcus aureus mastitis incows. Am. J. Vet. Res., 52(3):474-479.

[0468] Davis, R. S., Gluckman, P. D., Hart, I. C., and Henderson, H. V.,1987. Effects of injecting growth hormone or throixine on milkproduction and blood plasma concentrations of insulin-like growthfactors I and II in dairy cows. J. Endocrinol., 114:17-24.

[0469] Dehoff, M. H., Elgin, R. G., Collier, R. J., and Clemmons, D. R.,1988. Both type I an II insulin-like growth factor receptory bindingincrease during lactogenesis in bovine mammary tissue. Endocrinol.,122:2412-2417.

[0470] Dekkers, J. C. M. and Burnside, E. B., 1994. Sire selection forudder health. Ont. Milk Prod. Sept. 1994, pp. 31-34.

[0471] Dekkers, J. C. M., TenHag, J. H., and Weersink, A., 1996.Economic aspects of persistency of lactation. 1996 Dairy ResearchReport, Ontario Ministry of Agriculture, Food and Rural Affairs, Publ.No. 0396, pp. 81-85.

[0472] Dekkers, J. C. M., Kolstad, B., Jairath, L. K., and Liu, Z.,1996. A new economic index for selection of sires. 1996 Dairy ResearchReport, Ontario Ministry of Agriculture, Food and Rural Affairs, Publ.No. 0396, pp.15-25.

[0473] Dekkers, J. C. M., Boettcher, P. J., and Mallard, B. A., 1998.,Genetic improvement of udder health. 6th World Congr. Genet. Appl.Livest. Prod., Australia.

[0474] Derijk, R. and Sternberg, E. M., 1994. Corticosteroid action andneuroendocrine-immune interactions. Ann. N. Y. Acad. Sci., 746:33-44.

[0475] Detilleux, J. C., Koehler, K. J., Freeman, A. E., Kehrili, Jr.,M. E., and Kelley, D. H., 1994. Immunological parameters ofperiparturient Holstein cattle: genetic variation. J. Dairy Sci.,77:2640-2650.

[0476] Detilleux, J. C., Kehrli, Jr., M. E., Freeman, A. E., Whetston,C. A., and Kelley, D. H., 1995a. Two retroviral infections ofperiparturient Holstein cattle: A phenotypic and genetic study. J. DairySci., 78:2294-2298.

[0477] Detilleux, J. C., Kehrli, Jr., M. E., Stabel, J. R., Freeman, A.E., and Kelley, D. H., 1995b. Study of immunological dysfunction inperiparturient Holstein cattle selected for high and average milkproduction. Vet. Immunol. Immunopath., 44:251-267.

[0478] Detilleux, J. C., Kehrli, Jr., M. E., Stabel, J. R., Freeman, A.E., Fox, L., and Kelley, D. H. 1995c. Mastitis of periparturientHolstein cattle: A phenotypic and genetic study. J. Dairy Sci.,78:2285-2293.

[0479] Dietz, A. B., Cohen, N. D., and Timms, L., 1997a. Bovinelymphocyte antigen cl. II alleles as risk factors for high somatic cellcounts in milk of lactating dairy cows. J. Dairy Sci., 80:406-412.

[0480] Dietz, A. B., Detilleux, J. C., Freeman, A. E., Kelley, D. H.,Stabel, J. R. and Kehrli Jr., M. E., 1997b. Genetic association ofbovine lymphocyte antigen DRB3 alleles with immunological traits ofHolstein cattle. J. Dairy Sci., 80(2):400-405.

[0481] Dubey, D. P., 1992. Histocompabitiblity and transplantationimmunology. Manual of laboratory immunology. ASM Press. Editors: N. R.Rose, E. C. DeMacario, J. L. Fahey, H. Friedman, G. M. Penn., pp.870-871.

[0482] Dunklee, T. S., Freeman, A. E., and Kell, D. H., 1994. Comparisonof Holsteins selected for high and average milk production. 2. Healthand reproductive response to selection for milk. J. Dairy Sci.,77:3683-3690.

[0483] Dürr, J. W., Monardes, H. B. and Turner, J. D., 1996.Correlations of neutrophil phagocytosis for proven bulls with traits ofeconomic importance of their daughters. J. Dairy Sci., 79:133-139.

[0484] Durum, S. K., Schmidt, J., and Oppenheim, J. J., 1985.Interleukin-1: an immunological perspective. Ann. Rev. Immunol.,3:263-287.

[0485] Eichmann, K., Braun, D. G., and Krause, R. M., 1971. Influence ofgenetic factors on the magnitude and the heterogeneity of the immuneresponse in the rabbit. J. Exp. Med., 134:48-65

[0486] Elsasser, T. H., Rumsey T. S., Hammond, A. C., and Fayer, R.,1988. Influence of parisitism on plasma concentrations of growthhormone, somatomedin-C and somatomedin-binding proteins in calves. J.Endocrinol., 116:191-200.

[0487] Elsasser, T. H., Rumsey, T. S., and Hammond, A. C., 1989.Influence of diet on basal and growth hormone-stimulated plasmaconcentrations of IGF-I in beef cattle. J. Anim. Sci., 67:128-141.

[0488] Emmanuelson, U., Danell, B., and Phillipsson, J., 1988. Geneticparameters for clinical mastitis, somatic cell counts, and milkproduction estimated by multiple-trait restricted maximum likelihood. J.Dairy Sci., 71:467-475.

[0489] Envoldsen, C., Hindhede, J., and Kristensen, T., 1996. Dairy herdmanagement types assessed from indicators of health, reproduction,replacement, and milk production. J. Dairy Sci., 79:1221-1236.

[0490] Fornari, M. C., Scolnik, M. P., Palacios, M. F., Intebi, A. D.,and Diez, R.A., 1994. Growth hormone inhibits normal B celldifferentiation and neutrophils' chemotaxis in vitro. Int. J.Immunopharmac., 16(8):667-673.

[0491] Franco, P., Marelli, O., Lattuda, D., Locatelli, V., Cocchi, D.,and Muller, E. E., 1990. Influence of growth hormone on theimmunosuppressive effect of prednisolone in mice. Acta Endocrinologica,123:339-344.

[0492] Franklin, S. T., Young, J. W., and Nonnecke, B. J., 1991. Effectsof ketones, acetate, butyrate, and glucose on lymphocyte proliferation.,J. Dairy Sci., 74:2507-2514.

[0493] Fries, R., Hediger, R., and Stranzinger, G., 1986. Tentativechromosomal localization of the bovine majorhistocompatibility complexby in situ hybridization. Anim. Genet., 17:287-294.

[0494] Gambel, P. I., and Ferguson, F. G., 1982. An in vitro and in vivoanalysis of murine immunocompetence during pregnancy and lactation. J.Reprod. Immunol., 4:107-119.

[0495] Gavora, J. S., Spencer, J. L., Gowe, S., and Harris, D. L., 1980.Lymphoid leukosis virus infection: Effects on production, mortality, andconsequences for selection for high egg production. Poultry Sci.,59:2165-2178.

[0496] Gavora, J. S., and Spencer, J. L., 1983. Breeding for immuneresponsiveness and disease resistance. Anim. Blood Groups Biochem.Genet., 14:159-180.

[0497] Gawazdauskas, F. C., Keys, J. E., and McGilliard, M. L., 1986.Adrenal response during periparturient period to adrenocorticotropin indairy cattle fed corn silage and grass legume silage. J. Dairy Sci.,69:2134-2139.

[0498] Geffner, M. E., Bersch, N., Lippe, B. M., Rosenfeld, R. G.,Hintz, R. L., and Golde, D. W., 1990. Growth hormone mediates the growthof T-lymphoblast cell lines via locally generated insulin-like growthfactor-I. J. Clin. Endocrinol. Metab., 71:464-469.

[0499] Gilbert, R. O., Gröhn, Y. T., Miller, P. M., and Hoffman, D. J.,1993a. Effect of parity on periparturient neutrophil function in dairycows. Vet. Immunol. Immunopath., 36:75-82.

[0500] Gilbert, R. O., Gröhn, Y. T., Guard, C. L., Surman, V., Neilsen,N., and Slauson, D. O., 1993b. Impaired post partum neutrophil functionin cows which retain fetal membranes. Res. Vet. Sci., 55:15-19.

[0501] Gilbert, F. B., Poutrel, B. and Sutra, L., 1994. Immunogenecityin cows of Staphylococcus aureus type 5 capsularpolysaccharide-ovalbumin conjugate. Vaccine, 12(4):369-374.

[0502] Gillis, S., Ferris, M., Ou., W., and Smith, K., 1978. T cellgrowth factor: parameters for production and a quantitiative microassayfor activity. J. Immunol., 120:2027-2032.

[0503] Giraudo, J. A., Calzolari, A., Rampone, H., Rampone, A., Giraudo,A. T., Bogni, C., Larriestra, A., and Nagel, R., 1997. Field trials of avaccine against bovine mastitis. 1. Evaluation in heifers. J. DairySci., 80:845-853.

[0504] Glass, E. J., and Spooner, R. L., 1990. Generation andcharacterization of bovine antigen-specific T cell lines. J. Immunol.Methods, 128:267-275.

[0505] Glass, E. J., Oliver, R. A., and Spooner, R. L., 1990. Variationin T cell responses to ovalbumin in cattle: evidence for Ir genecontrol. Anim. Genet., 21:15-28.

[0506] Goff, J. P., and Horst, R. L., 1997. Phsiological changes atparturition and their relationship to metabolic disorders. J. DairySci., 80:1260-1268.

[0507] Goff, J. P., and Stabel, J. R., 1990. Decreased plasma retinol,α-tocopherol, and zinc concentration during the periparturient period:effect of milk fever. J. Dairy Sci., 73(11):3195-3199.

[0508] Gonzalez, R. L., Cullor, J. S., Jasper, J. E., Farver, T. B.,Bushnell, R. B. and Oliver, M. N., 1989. Prevention of clinical coliformmastitis in dairy cows by a mutant Escherichia coli vaccine. Can. J.Vet. Res., 53(3):301-305.

[0509] Gray, G. D., Knight, K. A., Nelson, R. D., and Herron, M. J.,1982. Chemotactic requirements of bovine leukocytes. Am. J. Vet. Res.,43: 757-759.

[0510] Griffin, J. F. T., 1989. Stress and Immunity: a Unifying Concept.Vet. Immunol. Immunopathol., 20: 263-312.

[0511] Gröhn, Y. T., Eicker, S. W., and Hertl, J. A. 1995. Theassociation between previous 305-day milk yield and disease in New Yorkstate dairy cows. J. Dairy Sci., 78:1693-1702.

[0512] Guidry, A. J., Paape, M. J., and Pearson, R. E., 1976. Effects ofparturition and lactation on blood and milk cell concentrations,corticosteroids, and neutrophil phagocytosis of the cow. Am. J. Vet.Res., 37:1195-1200.

[0513] Gupta, S., Fikrig, S. M., and Noval, M. S., 1983. Immunologicalstudies in patients isolated with growth hormone deficiency. Clin. Exp.Immunol., 54:87-90.

[0514] Harmon, R. J., Schanbacker, F. L., Ferguson, L. C., and Smith, K.L., 1975. Concentration of lactoferrin in milk of normal lactating cowsand changes occurring during mastitis. Am. J. Vet. Res., 36:1001-1007.

[0515] Harmon, R. J., 1994. Physiology of mastitis and factors affectingsomatic cell counts. 1994. J. Dairy Sci., 77: 2103-2112.

[0516] Harp, J. A., Kehrli, M. E., Hurley, D. J., Wilson, R. A., andBoone, T. C., 1991. Number and percent of T lymphocytes in bovineperipheral blood during the peripartum period. Vet. Immunol.Immunopath., 28:29-35.

[0517] Helwig, T. T. and Council, K. A., 1982. SAS User's Guide. SASInstitute, Raleigh, N.C.

[0518] Hein, W. R. and Mackay, C. R., 1991. Prominence of γδ T cells inthe ruminant immune system. Immunol. Today, 12:30-34.

[0519] Hessing, M. J. C., Coenen, G. J., Vaiman, M., and Renard, C.,1995. Individual differences in cell-mediated and humoral immunity inpigs. Vet Immunol. Immunopath., 45:97-113.

[0520] Heyneman, R., Burvenich, C., and Vercauteren, R., 1990.Interaction between the respiratory burst activity of neutrophils,leukocytes and experimentally induced Escherichia coli mastitis in cows.J. Dairy Sci., 73:985-994.

[0521] Hill, A. W., Heneghan, D. J. S., and Williams, M. R., 1983. Theopsonic activity of bovine milk whey for the phagocytosis and killing byneutrophils of encapsulated and non-encapsulated Escherichia coli. Vet.Microbiol., 8:293-300.

[0522] Hoffman-Goetz, L. and Pedersen, B. K., 1994. Exercise and theimmune system: a model of the stress response? Immunol. Today,15(8):382-387.

[0523] Hoshino, S., Wakita, M., Kobayashi, Y., Sakauchi, R., Nishiguchi,Y., Ozawa, A., Hodate, K., Hamaguchi, I. and Yotani, Y., 1991.Variations in serum level of insulin-like growth factor-1, growthhormone, and thyroid hormones during lactation in dairy cows. Comp.Biochem. Physiol., 99(12):61-64.

[0524] Hutt, F. B., 1958. Genetic resistance to disease in domesticanimals. Ithaca, N.Y., Cornell University Press; London., 198pp.

[0525] Ibanez, O. M., Reis, M. S., Gennari, M., Ferreira, V. C. A,Sant'Anna, O. A., Siqueira, M., and Biozzi, G., 1980. Selective breedingof high and low antibody-responder lines of guinea pigs. Immunogenet.,10:283-293.

[0526] Ishikawa, H., 1983. Depression of B lymphocytes by mastitis andtreatment with levamisole. J. Dairy Sci., 66:556-561.

[0527] Ishikawa, H., 1987. Observation of lymphocyte function inperinatal cows and neonatal calves. Jpn. J. Vet. Sci., 49(3):469-475.

[0528] Kashiwazaki, Y., 1984. Lymphocyte activities in dairy cows withspecial preference to outbreak of mastitis pre-and postpartus. Jpn. J.Vet. Res., 32:101.

[0529] Kashiwazaki, Y., 1985. Transformation of bovine peripheral bloodlymphocytes in the perinatal period. Jpn. J. Vet. Sci., 47(2):337-339.

[0530] Kehrli, M. E., Jr., Nonnecke, B. J., and Roth, J. A., 1989a.Alterations in bovine lymphocyte function during the periparturientperiod. Am. J. Vet. Res., 50(2):215-220.

[0531] Kehrli, M. E. Jr, Nonnecke, B. J., and Roth, J. A., 1989b.Alterations in bovine neutrophil function during the periparturientperiod. Am. J. Vet. Res., 50(2):207-214.

[0532] Kehrli, M. E. Jr., Goff, J. P., Harp, J. A., Thurston, J. R.,Norcross, N. L., 1990a. Effects of preventing periparturienthypocalcemia in cow by parathyroid hormone administration on hematology,conglutinin, immunoglobulin and shedding of Staphylococcus aureus inmilk. J. Dairy Sci., 73(8):2103-2111.

[0533] Kehrli, M. E., Schmalstieg, F. C., Anderson, D. C., Van DerMaaten M. J., Hughes, B. J., Ackermann, M. R., Willhelmsen, C. L.,Brown, G. B., Stevens, M. G., and Whetstone, C. A., 1990b. Moleculardefinition of the bovine granulocytopathy syndrome: Identification ofdeficiency of the Mac-I (CD11b/CD18) glycoprotein. Am. J. Vet. Res.,51:1826-1836.

[0534] Kehrli, M. E., Jr., Weigel, K. E., Freeman, A. E., Thurston, J.R., and Kelley, D. H., 1991. Bovine sire effects on daughters' in vitroblood neutrophil functions, lymphocyte blastogenesis, serum complementand conglutinin levels. Vet. Immunol. Immunopath., 27:303-319.

[0535] Kelley, K. W., 1990. The role of growth hormone in modulation ofthe immune response. Ann. N.Y. Acad. Sci., 594:95-103.

[0536] Kelley, K. W., Greenfield, R. E., Evermann, J. F., Parish, S. M.,and Perryman, L. E., 1982. Delayed-type hypersensitivity, contacthypersensitivity, and phytohemagglutinin skin-test responses of heat andcold-stressed calves. Am. J. Vet. Res., 43(5):775-779.

[0537] Kelm, S. C., Detilleux, J. C., Freeman, A. E., Kehrli, Jr., M.E., Dietz, A. B., Fox, L. K., Butler, J. E., Kasckovics, I., and Kelley,D. H. Genetic association between parameters of innate immunity andmeasures of mastitis in periparturient Holstein cattle. J. Dairy Sci.(submitted).

[0538] Kensinger, M. H., Hurley, D. J., and Wilson, R. A., 1990. Cultureconditions for blastogenic responses of bovine mammary mononuclearcells. Vet. Immunol. Imnmunopath., 24:323-330.

[0539] Khansari, D. N., Murgo, A. J., and Faith, R. E., 1990. Effects ofstress on the immune system. Immunol. Today, 11(5):170-175.

[0540] Kollmann, D., 1993. Thesis: Eimeria infections in cows and theircalves during the periparturient phase. Veterinarmedizin, pp. 1-150. (inGerman, with English abstract).

[0541] Kremer, W. D. J., Noordhuizen-Stassen, E. N., and Lohuis, J. A.C. M. 1990. Host defence mechanisms and bovine coliform mastitis—areview. Vet. Quart. 12(2):103-113.

[0542] Kuby, J., 1997. Immunology. Third edition., W. H. Freeman andCompany, USA.

[0543] Larsen, B., Jensen, N. E., Madsen, P., Nielsen, S. M., Klastrup,O. and Madsen, P. S., 1985. Association of the M blood group system withbovine mastitis. Anim. Blood Groups Biochem. Genet., 16:165-173.

[0544] Lewin, H., 1989. Disease resistance and immune response genes incattle: Strategies for their detection and evidence of their existence.J. Dairy Sci., 72(50):1334-1348.

[0545] Lewin, H., 1994., Host genetic mechanism of resistance andsusceptibility to a bovine retroviral infection. Anim. Biotech.,5(2):183-191.

[0546] Liacopoulos-Briot, M., Bouthillier, Y., Mouton, D., Lambert, F.,Dcreusefond, C., Stiffel, C., and Biozzi, G., 1972. Comparison of skinallograft rejection and cytotoxic antibody production in tow line osmice genetically selected for ‘high’ and ‘low’ antibody synthesis.Transplant., 14:590-596

[0547] Liacopoulos-Briot, M., Lambert, F., Mouton, D., Bouthillier, Y.,Decreusefond, C., Stiffel, C., and Biozzi, G., 1972. Stimulation deslymphocytes par la phytohémagglutinine chez les souris des lignéesgénétiquement sélectionnées d'après le caractère ‘productiond'anticorps’. Ann. Immunol., Paris, 123:135.

[0548] Lie, Ø. 1979. Genetic analysis of some immunological traits inyoung bulls. Acta Vet. Scand., 20:372-386.

[0549] Lie, Ø., 1985. Genetic approach to mastitis control. Kiel.Milchwirtsch. Forshungsber., 37:487.

[0550] Lie, Ø., Solbu, H., Larsen, H. J., and Spooner, R. L., 1986.Possible association of antibody responses to human serum albumin and(T,G)-A—L with the bovine major histocompatibility complex (BoLA). Vet.Immunol. Immunopath., 11:333-350.

[0551] Logg, M. H., 1942. Effect of pregnancy and parturition onpulmonary tuberculosis. Br. Med. J., 1:468-469.

[0552] Lundén, A., Sigurdardottir, S., Edfors-Lilja, I., Danell, B.,Rendel, J. and Andersson, L., 1990. The relationship between bovinemajor histocompatibility complex class II polymorphism and diseasestudied by use of bull breeding values. Anim. Genet., 21:221-232.

[0553] Luije, V. and Black, S. J., 1991. Cellular interactionsregulating the in vitro response of bovine lymphocytes to ovalbumin.Vet. Immunol. Immunopath., 28(3-4):275-288.

[0554] MacPhee, I. A. M., Antoni, F. A., and Mason, D. W., 1989.Spontaneous recovery of rats from experimental allergicencephalomyelitis is dependent on regulation of the immune system byendogenous adrenal corticosteroids. J. Exp. Med., 169:431-445.

[0555] Madsen, P., 1989. Genetic resistance to bovine mastitis. Curr.Top. Vet. Med. Anim. Sci., 52:169-177.

[0556] Mallard, B. A., Wilkie, B. N., and Kennedy, B. W., 1989. Geneticand other effects on antibody and cell mediated immune response inSLA-defined miniature pigs. Anim. Genet., 20:167-178.

[0557] Mallard, B. A., Wilkie, B. N., Kennedy, B. W., and Quinton, M.,1992. Use of Estimated Breeding Values in a selection index to breedYorkshire pigs for high and low immune and innate resistance factors.Anim. Biotech., 3(2):257-280.

[0558] Mallard, B. A., Wagter, L. C., Ireland, M. J., and Dekkers, J. C.M., 1997. Effects of growth hormone, insulin-like growth factor I, andcortisol on periparturient antibody response profiles of dairy cattle.Vet. Immunol. inmunopath. (accepted for publication).

[0559] Mallard, B. A., Wilkie, B. N., Kennedy, B. W., Gibson, J., andQuinton, M., 1998. Immune responsiveness in swine: Eight generations ofselection for high and low immune response in Yorkshire pigs. 6th WorldCongr. Genet. Appl. Livest. Prod., Australia.

[0560] Mallard, B. A., Leslie, K. E., Dekkers, J. C. M., Hedge, R.,Bauman, M., and Stear, M. J., 1995. Differences in bovine lymphocyteantigen associations between immune response and risk of diseasefollowing intramammary infection with Staphylococcus aureus. J. DairySci., 78:1937

[0561] Mallard, B. A., Dekkers, J. C., Ireland, M. J., Leslie, K. E.,Sharif, S, Lacey-Van Kampen, C., Wagter, L. and Wilkie, B. N., 1997.Alteration in immune responsiveness during the peripartum period. J.Dairy Sci. (accepted for publication).

[0562] Malo, D., Hu, J., Skamene, E., and Schurr, E., 1994. Populationand molecular genetics of susceptibility to intracellular pathogens.Anim. Biotech., 5(2):173-182.

[0563] Manak, R. C., 1982. Mitogenic responses of peripheral bloodlymphocytes from pregnant and ovariectomized heifers and theirmodulation by serum. J. Reprod. Immunol., 4:263-276.

[0564] Martin, W., Meek, A., and Willeberg, P., 1987. VeterinaryEpidemiology: principles and methdods. 1^(st) Edition. Iowa StateUniversity Press. pp. 129-131.

[0565] Mason, D., 1991. Genetic variation in the stress response:susceptibility to experimental allergic encephalomyelitis andimplication for human inflammatory disesase. Immunol. Today,12(2):57-60.

[0566] Matthews, K. R., Harmon, R. J. and Langlois, B. E., 1992.Prevalence of Staphylococcus species during the periparturient period inprimiparous and multiparous cows. J. Dairy Sci., 75:1835-1839.

[0567] McClure, A. M., Christopher, E. E., Wolff, W. A., Fales, W. H.,Krause, G. F., and Miramonti, J., 1994. Effect of Re-17 mutantSalmonella typhimurium bacterin toxoid on clinical coliform mastitis. J.Dairy Sci., 77:2272-2280.

[0568] Mejdell, C. M., Lie, Ø., Solbu, H., Arnet, E. F., and Spooner, R.L., 1994. Association of major histocompatibility complex antigens(BoLA-A) with AI bull progeny test results for mastitis, ketosis, andfertility in Norwegian cattle. Anim. Genet., 25:99-104.

[0569] Miglior, F., Burnside, E. B., and Dekkers, J. C. M., 1995.Nonadditive genetic effects and inbreeding depression for somatic cellcounts of Holstein cattle. J. Dairy Sci., 78:1168-1173.

[0570] Mouton, D., Bouthillier, Y., Oriol, R., Decreusefond, C.,Stiffel, C., and Biozzi, G., 1981. Intensité de la réactiond'hypersensibilité retardée chez les souris des lignées sélectionnées<<bonnes>>et <<mauvaises>>productrices d'anticorps. Ann. Immunol.,Paris, 125C:581-588.

[0571] Morrow-Tesch, J. L., McGlone, J. J., and Norman, R. L., 1993.Consequences of restraint on natural killer cell activity, behaviour,and hormone levels in rhesus macaques. Psychoneuroendocrinol.,18(5-6):383-385.

[0572] Morrow-Tesch, J. L., Wollen, N., and Hahn, L., 1996. Response ofγδ T-lymphocytes to heat stress in Bos taurus and Bos indicus crossbredcattle. J. Therm. Biol., 21(2):101-108.

[0573] Myllys, V. and Rautala, H., 1995. Characterization of clinicalmastitis in primiparous heifers. J. Dairy Sci., 78:538-545.

[0574] Nardone, A., Lacetera, N., Bernabucci, U., and Ronchi, B., 1997.Composition of colostrum from dairy heifers exposed to high airtemperatures during late pregnancy and the early postpartum period. J.Dairy Sci., 80:838-844.

[0575] Nagahata, H., Ogawa, A., Sanada, Y., Noda, H., and Yamamota, S.,1992. Peripartum changes in antibody producing capability of lymphocytesfrom dairy cows. Vet. Quart., 14(1):39-40.

[0576] Nagahata, H., Makino, S., Takeda, S., Takahashi, H., and Noda,H., 1988. Assessment of neutrophil function in the dairy cow during theperinatal period. J. Vet. Med., 35:747-751.

[0577] Nakao, T., and Grunnert, E., 1990. Adrenocortical function incows with the downer cow syndrome. A preliminary report. J. Vet. Med.,37(8):610-613.

[0578] Nardone, A., Lacetera, N., Bernabucci, U., and Ronchi, B., 1997.Composition of colostrum from dairy heifers exposed to high airtemperatures during late pregnancy and the early postpartum period. J.Dairy Sci., 80:838-844.

[0579] Nash, M. S., 1994. Excercise and immunology. Med. Sci. SportsExerc., 26(2):125-127.

[0580] National Mastitis Council. 1994. Udder Topics, 17(4):1-4.

[0581] Newbould, F. H. S., 1976. Phagocytic activity of bovineleukocytes during pregnancy. Can. J. Comp. Med., 40:111-116.

[0582] Nickerson, S. C., Owens, W. E., Boddie, R. L., and Boddie, N. T.,1992. The effect of chronic immunostimulation of the nonlactating bovinemammary gland with interleukin-2, pokeweed mitogen andlipopolysaccharide. J. Dairy Sci., 75(12):3339-3351.

[0583] Nickerson, S. C., 1993. Vaccination programs for preventing andcontrolling mastitis. Natl. Mast. Coun. Reg. Mtg. Proc., pp. 64-72.

[0584] Nickerson, S. C., Owens, W. E., Rejman, J. J., and Oliver, S. P.,1993. Effects of interleukin-1 and interleukin-2 on mammary glandleukocyte populations and histology during the early nonlactationperiod. J. Vet. Med., 40:621-633.

[0585] Nickerson, S. C., Baker, P. A., and Trinidad, P., 1989. Localimmunostimulations of the bovine mammary gland with interleukin-2.J.Dairy Sci., 72:1764-1773.

[0586] Nielson, U. S., Pedersen, G. A., Pedersen, J., and Jensen, J.1997., In Proc. Genet. Improvement Func. Traits in Cattle., Uppsula,Sweden.

[0587] Nonnecke, B. J., and Harp, J. A., 1985. Effect of chronicstaphylococcal mastitis on mitogenic responses of bovine lymphocytes. J.Dairy Sci., 68:3323-3328.

[0588] de Oliveira, S. L., Ibanez, O. M., Mouton, D., Sant'Anna, O. A.,Siqueira, M., and Biozzi, G., 1985. Independent polygenic regulation ofquantitative antibody responsiveness and expression of delayed-typehypersensitivity (DTH). Expl. Clin. Immunogenet., 2:223-233.

[0589] Ontario Milk Producer, February 1997. Prices., p.30.

[0590] Oppenheim, J. J., Kovacs, E. J., Matsuchima, K., and Durams, S.K., 1986. There is more than one interleukin-1. Immunol. Today, 7:45-56.

[0591] Paape, M. J., Hafs, H. D. and Snyder, W. W., 1963. Variation ofestimated numbers of milk somatic cells stained with wright's stain orpyronin y-methyl green stain. J. Dairy Sci., 46: 1211-1216.

[0592] Park, Y. H., Fox, L. K., Hamilton, M. J. and Davis, W. C., 1992.Bovine mononuclear leukocyte subpopulations in peripheral blood andmammary gland secretions during lactation. J. Dairy Sci., 75:998-1006.

[0593] Parker, K. A., Leyh, R., Field, M. F., and Anderson, G. A., 1994.Serologic response of cattle to core antigen vaccination. Natl. Mast.Coun. Ann. Mtg. Proc., pp. 326-327.

[0594] Peter, A. T. and Bosu, W. T. K., 1987. Peripartal endocrinechanges associated with retained placenta in dairy cows. Theriogenol.,28:383-393.

[0595] Petersen, B. H., Rapaport, R., Henry, D. P., Huseman, C., andMoore, W. V., 1990. Effect of treatment with biosynthethic human growthhormone (GH) on peripheral blood lymphocyte populations and function ingrowth hormone-deficient children. J. Clin. Endocrinol. Metab.,70:1756-1760.

[0596] Pinard, M. H. and Van der Zijpp, A. J., 1992. Divergent selectionfor immune resposiveness in chickens: Estimation of realizedheritability with an animal model. J. Anim. Sci., 70(10):2986-2993.

[0597] Politis, I., Hidiroglou, M., Batra, T. R., Gilmore, J. A.,Gorewit, R. C., and Scherf, H., 1995. Effects of vitamin E on immunefunction of dairy cows. Am. J. Vet. Res., 56(2):179-184.

[0598] Puel, A, and Mouton, D., 1996. Genes responsible for quantitativeregulation of antibody production. Crit. Rev. Immunol., 16:223-250.

[0599] Reents, R., Dekkers, J. C. M., and Schaeffer, L. R., 1995.Genetic evaluation for somatic cell score with a test day model formultiple lactations. J. Dairy Sci., 78:2858-2870.

[0600] Rich, A. R., 1951. The influence of sex and age. In: Thepathogenesis of tuberculosis. 2nd ed. Springfield: Charles C Thomas. pp.189-195.

[0601] Rogers, M. P., Dubey, D., and Rech P., 1979. The influence ofpsyche and the brain on immunity and disease susceptibility: a criticalreview. Psychosom. Med., 41:147-164.

[0602] Romagnani, S., 1997. The Th1/Th2 paradigm. Immunol. Today,18(6):263-266.

[0603] Roth, J. A., and Kaeberle, M. L., 1982. Effect of glucocorticoidson the bovine immune system. J. Am. Vet. Med. Assoc., 180:894-901.

[0604] Saad, A. M., Concha, C., and Astrom, G., 1989. Alterations inneutrophil phagocytosis lymphocyte blastogenesis in dairy cows aroundparturition. J. Vet. Med., 36:337-345.

[0605] Sandholm, M. and Mattila, T., 1986. Mechanisms of infection andinflammation of the mammary gland- an overview. Proceedings of symposionon mastitis control and hygienic production of milk. Espoo, Finland.

[0606] Petersen, B. H., Rapaport, R., Henry, D. P., Huseman, C., andMoore, W. V., 1990. Effect of treatment with biosynthethic human growthhormone (GH) on peripheral blood lymphocyte populations and function ingrowth hormone-deficient children. J. Clin. Endocrinol. Metab.,70:1756-1760.

[0607] Pinard, M. H. and Van der Zijpp, A. J., 1992. Divergent selectionfor immune resposiveness in chickens: Estimation of realizedheritability with an animal model. J. Anim. Sci., 70(10):2986-2993.

[0608] Politis, I., Hidiroglou, M., Batra, T. R., Gilmore, J. A.,Gorewit, R. C., and Scherf, H., 1995. Effects of vitamin E on immunefunction of dairy cows. Am. J. Vet. Res., 56(2):179-184.

[0609] Puel, A, and Mouton, D., 1996. Genes responsible for quantitativeregulation of antibody production. Crit. Rev. Immunol., 16:223-250.

[0610] Reents, R., Dekkers, J. C. M., and Schaeffer, L. R., 1995.Genetic evaluation for somatic cell score with a test day model formultiple lactations. J. Dairy Sci., 78:2858-2870.

[0611] Rich, A. R., 1951. The influence of sex and age. In: Thepathogenesis of tuberculosis. 2nd ed. Springfield: Charles C Thomas. pp.189-195.

[0612] Rogers, M. P., Dubey, D., and Rech P., 1979. The influence ofpsyche and the brain on immunity and disease susceptibility: a criticalreview. Psychosom. Med., 41:147-164.

[0613] Romagnani, S., 1997. The Th1/Th2 paradigm. Immunol. Today,18(6):263-266.

[0614] Roth, J. A., and Kaeberle, M. L., 1982. Effect of glucocorticoidson the bovine immune system. J. Am. Vet. Med. Assoc., 180:894-901.

[0615] Saad, A. M., Concha, C., and Astrom, G., 1989. Alterations inneutrophil phagocytosis lymphocyte blastogenesis in dairy cows aroundparturition. J. Vet. Med., 36:337-345.

[0616] Sandholm, M. and Mattila, T., 1986. Mechanisms of infection andinflammation of the mammary gland—an overview. Proceedings of symposionon mastitis control and hygienic production of milk. Espoo, Finland.

[0617] Schaeffer, L. R., Minder, C. E., McMillan, I., and Burnside, E.B., 1977. Nonlinear techniques for predicting 305-day lactationproduction of Holsteins and Jerseys. J. Dairy Sci., 60:1636-1644.

[0618] Schiebel, I. F., 1943. Hereditary differences in the capacity ofguinea-pigs for the production of diptheria antitoxin. Acta Path.Microbiol. Scand., 20:464-484.

[0619] Schukken, Y. H., Mallard, B. A., Dekkers, J. C. M., Leslie, K.E., and Stear, M. J., 1994. Genetic impact on the risk of intramammaryinfection following Staphylococcus aureus challenge. J. Dairy Sci.,77:639-647.

[0620] Schmutz, S. M., Berryere, T. G., Robbins, J. W., and Carruthers,T. D., 1992. Resistance to Staphylococcus aureus mastitis detected by aDNA marker. Proc. Nation. Mast. Council. pp. 124-133.

[0621] Schurmann, A., Spencer, G. S. G., and Berry, C. J., 1995. Growthhormone alters lymphocyte sub-populations and antibody production indwarf rats in vivo. Experientia, 51:780-785.

[0622] Schutz, M. M., 1994. Genetic evaluation of somatic cell scoresfor united states dairy cattle. J. Dairy Sci., 77:2113-2129.

[0623] Sharif, S., Mallard, B. A., Wilkie, B. N., Sargeant, J. M.,Scott, H. M., Dekkers, J. C. M., and Leslie, K. E., 1997. Associationsof the bovine major histocompatibility complex DRB3 (BoLA-DRB3) alleleswith occurrence of disease and milk somatic cell score in Canadian dairycattle., Anim. Genet. (submitted).

[0624] Shook, G. E., 1989. Selection for disease resistance. J. DairySci., 72:1349-1362.

[0625] Shook, G. E., 1993. Genetic Improvement of mastitis throughselection on somatic cell count. Veterinary clinics of north america:Food animal practice, 9(3):563-580.

[0626] Shook, G. E. and Schutz, M. M., 1994. Selection on somatic cellscore to improve resistance to mastitis in the United States. J. DairySci., 77:648-658.

[0627] Shuster, D. E., Lee, E. K., and Kehrli, M. E., 1996. Bacterialgrowth, inflammatory cytokine production and neutrophil recruitmentduring coliform mastitis in cows within ten days after calving, comparedwith cows at midlactation. Am. J. Vet. Res., 57(11):1569-1575.

[0628] Siegel, B. P., and Gross, W. B., 1980. Production and persistenceof antibodies in chickens to sheep erythrocytes. 1. Directionalselection. Poultry Sci., 59:1-5.

[0629] Sigguradóttir, S., Lunden, A., and Andersson, L., 1988.Restriction fragment length polymorphis of DQ and DR class II genes ofthe bovine major histocompatibility complex. Anim. Genet., 19:133-150.

[0630] Smith, K. L., and Hogan, J. S., 1994. Understanding environmentalmastitis. Natl. Mast. Coun. Reg. Mtg. Proc., pp. 33-38.

[0631] Smith, K. L., Todhunter, D. A., and Schoenberger, P. S., 1985.Environmental pathogens and intramammary infection during the dryperiod. J. Dairy Sci., 68:402-417.

[0632] Smith, V. G., Edgerton, L. A., Hafs, H. D., and Convey, E. M.,1973. Bovine serum estrogens progestins, and glucocorticoids during latepregnancy, parturition, and early lactation. J. Anim. Sci., 36:391-396.

[0633] Solbu, H., Spooner, R. L. and Lie, O. 1982. A possible influenceof the bovine major histocompatibility complex (BoLA) on mastitis. Proc.2nd World Congr. Genet. Appl. Livest. Prod., 7:368.

[0634] Soller, M., 1994. Marker assisted-selection-an overview. Anim.Biotech., 5:193-207.

[0635] Sordillo, L. M., 1995. Vaccination as a protection against bovinemastitis. Natl. Mast. Coun. Reg. Mtg. Proc., pp. 64-68.

[0636] Sordillo, L. M., and Babiuk, L. A., 1991. Controlling acuteEscherichia coli mastitis during the periparturient period withrecombinant bovine interferon gamma. 1991. Vet. Microbiol., 28:189-198.

[0637] Sordillo, L. M., Redmon, M., Campos, M., Warren, L., and Babiuk,L. A., 1992. Cytokine activity in bovine mammary gland secretions duringthe periparturient period. Can. J. Vet. Res., 55:298-301.

[0638] Spangelo, B. L. and Gorospe, W. C., 1995. Role of cytokines inthe neuroendocrine-immune system axis. Front. Neuroendocrinol.,16(1):1-22.

[0639] Sridama, V., Pacini, F., Yang, S -L., Moawad, A., Reilly, M., andDeGroot, L. J., 1982. Decreased levels of helper T cells. N. Engl. J.Med., 307:352-356.

[0640] Sternberg, E. M., Hill, J. M., Chrousos, G. P., Kamilarus, T.,Listwak, S. J., Gold, P. W., and Wilder, R. L., 1989. Inflammatorymediator-induced hypothalamic-pituitary-adrenal axis activation isdefective in streptococcal cell wall arthritis-susceptible Lewis rats.Proc. Natl. Acad. Sci. USA, 86(7):2374-2378.

[0641] Sulimova, G. E., Udina, I. G., Shaikhaev, G. O., and Zakharov, I.A., 1995. DNA polymorphism at the BoLA-DRB3 gene of cattle in relationto resistance to susceptibility to leukemia. Russ. J. Genet.,31:1105-1109.

[0642] Todhunter, D. A., Smith, K. L., and Hogan, J. S., 1990. Growth ofGram-negative bacteria in dry cow secretions. J. Dairy Sci., 73:363-372.

[0643] Van der Zijpp, A. J., Frankena, K., Boneschanscher, J., andNieuwland, M. G. B., 1983. Genetic analysis of primary and secondaryimmune response in the chicken. Poultry Sci., 6: 565-572.

[0644] Van Kampen, C. and Mallard, B. A., 1997. Effects of peripartumstress and disease on bovine lymphocyte subsets. Vet. Immunol.Immunopath. (Accepted for publication).

[0645] Van Werven, T., Noordhuizen-Stassen, E. N., Daemen, A. J. J. M.,Schukken, Y. H., Brand, A. and Burvenich, C., 1997. Preinfection invitro chemotaxis, phagocytosis, oxidative burst, and expression ofCD11/CD18 receptors and their predictive capacity on the outcome ofmastitis induced in dairy cows with Escherichia coli. J. Dairy Sci.,80:67-74.

[0646] Vassilopoulou-Sellin, R., 1994. Endocrine effects of cytokines.Oncology. Huntingt., 8(10):4 3-46.

[0647] Vega, J. R., Gibson, C. A., Skaar, T. C., Hadsell, D. L. andBaumrucker, C. R., 1991. Insulin-ike growth factor (IGF) 1 and 2 and IGFbinding proteins in serum and mammary secretions during the dry periodand early lactation in dairy cows. J. Anim. Sci., 69:2538-2547.

[0648] Watson, D. L., and Schwartskoff, C. L., 1990. A field trial totest the efficacy of a staphylococcal mastitis vaccine in commercialdairies in Australia. In: Proc. Int. Symp. Bov. Mast., Indianapoli,Ind., pp.73.

[0649] Weigel, K. A., Kehrli, Jr., M. E., Freeman, A. E., Thurston, J.R., Stear, M. J., and Kelley, D. H., 1991. Associations of class Ibovine lymphocyte antigen complex alleles with in vitro blood neutrophilfunctions, lymphocyte blastogenesis, serum complement and conglutininlevels in dairy cattle. Vet. Immunol. Immunopath., 27:321-335.

[0650] Weiland, F., and Straub, O. C., 1976. Differences in the in vitroresponse of lymphocytes from leukotic and normal cattle to concanavalinA. Res. Vet. Sci., 20:340-341.

[0651] Weinberg, E. D., 1984. Pregnancy-associated depression ofcell-mediated immunity. Rev. Inf. Dis., 6(6):814-831.

[0652] Wells, P. W., Burrells, C., and Martin, W. B., 1977. Reducedmitogenic responses in cultures of lymphocytes from newly calved cows.Clin. Exp. Immunol., 29:159-161.

[0653] Weller, J. J., Saran, A., and Zeliger, Y., 1992. Genetic andenvironmental relationships among somatic cell count, bacterialinfection, and clinical mastitis. J. Dairy Sci., 75:2532-2540.

[0654] Wijngaard, P. L. J., Metzelaar, M. J., MacHugh, N. D., Morrison,W. I., and Clevers, H. C., 1992. Molecular characterization of the WC1antigen expressed specifically on bovine CD4-CD8-γδ T lymphocytes. J.Immunol., 149:3273-3277.

[0655] Williams, M. R., and Hill, A. W., 1982. A role for IgM in the invitro opsonisation of Staphylococcus aureus and Escherichia coli bybovine polymorphonuclear leucocytes. Res. Vet. Sci., 33:47-53.

[0656] Wright, P., 1987. Enzyme Immunoassay: observations on aspects ofquality control. Vet. Immunol. Immunopath., 17:441-452.

[0657] Xu, A., van Eijk, M. J. T., Park, C., and Lewin, H. A., 1993.Polymorphism in BoLA-DRB3 exon 2 correlates with resistance topersistent lymphocytosis caused by bovine leukemia virus. J. Immunol.,151:6977-6985.

[0658] Yang, T. J., Mather, J. F., and Rabinovsky, E. D., 1988. Changesin subpopulations of lymphocytes in peripheral blood, and supramammaryand prescapular lymph nodes of cows with mastitis and normal cows. Vet.Immunol. Immunopath., 18:279-285.

[0659] Yoshida, A., Ishioka, C., Kimata, H., and Mikawa, H., 1992.Recombinant human growth hormone stimulates B cell immunoglobulinsynthesis and proliferation in serum-free medium. Acta Endocrinologica,126:524-529.

[0660] Zanotti, M., Poli, G., Ponti, W., Polli, M., Rocchi, M., Bolzani,E., Longeri, M., Russo, S., Lewin, H. A., van Eijk, M. J. T., 1996.Association of BoLA class II haplotypes with subclinical progression ofbovine leukemia virus infection in Holstein-Friesian cattle. Anim.Genet. 27:337-341.

[0661] Zhang, W. C., Dekkers, J. C. M., Banos, G., and Burnside, E. B.,1993. Sire genetic evaluation for somatic cell score and relationshipswith other traits. 1993 Dairy Research Report, Ontario Ministry ofAgriculture, Food and Rural Affairs, Publ. No. 0193, pp., 106-109.

DETAILED FIGURE LEGENDS

[0662]FIG. 1. LS Means of antibody response to OVA in A) serum and B)whey by antibody response group following immunization at weeks −8, −3,and 0 as measured by enzyme linked immunosorbent assay (ELISA). Group1=high measurable antibody response; Group 2=lack of measurable responseto immunization postpartum (week 0); Group 3=lack of measurable responseto immunization pre- and postpartum; Pop=population mean. Animalclassification is based on serum antibody response to OVA. Significantdifferences between animals in the three groups are indicated bydifferent letters above error bars (P≦0.05).

[0663]FIG. 2. Percent disease occurrence by antibody response group.Group 1=high measurable antibody response; Group 2=lack of measurableresponse to immunization postpartum (week 0); Group 3=lack of measurableresponse to immunization pre- and postpartum. Animal classification isbased on serum antibody response to ovalbumin (OVA).

[0664]FIG. 3. LS Means of serum antibody response to ovalbumin (OVA) byantibody response group. Group 1=high antibody response, Group 2=averageantibody response, and Group 3=low antibody response based on describedindex, and Population mean (PM). Significant differences between groupsare indicated with lower case letters between groups and differencesover time are indicated by different uppercase letters (P<0.05).

[0665]FIG. 4. LS Means of whey antibody response to ovalbumin (OVA) byantibody response group for A) Herd 1, B) Herd 2 and C) Herd 3. Group1=high antibody response, Group 2=average antibody response, and Group3=low antibody response based on described index, and Population mean(PM). Significant differences between groups are indicated with lowercase letters and differences over time are indicated by differentuppercase letters (P<0.05).

[0666]FIG. 5. LS Means of sera antibody response to E. coli for A) Herd1, B) Herd 2 and C) Herd 3. Group 1=high antibody response, Group2=average antibody response, and Group 3=low antibody response based ondescribed index, and population mean (PM). Significant differencesbetween groups are indicated with different lower case letters (P<0.05).

[0667]FIG. 6. LS Means of IgG₁ in A) sera and B) whey. Group 1=highantibody response, group 2=average antibody response, and Group 3=lowantibody response based on described index, and Population mean (PM).Significant differences between groups are indicated with lower caseletters and differences over time are indicated by different upper caseletters(P<0.05).

[0668]FIG. 7. Rate of Mastitis occurrence (%) by antibody response groupwithin herd.

[0669]FIG. 8. LS Means of Somatic Cell Score by antibody response groupfor A) Herd 1; B) Herd 2; and C) Herd 3. Group 1=high antibody response,Group 2=average antibody response, and Group 3=low antibody responsebased on described index, and Population mean (PM). Significantdifferences between groups are indicated with different lower caseletters (P<0.05).

[0670]FIG. 9. Type III LS Means of counts per minute (cpm) measuringunstimulated (1A) and stimulated lymphocyte proliferation to ovalbumin(OVA; 1B) and concanavalin A (Con A; 1C). Group 1=high antibody responseto OVA, Group 2=average antibody response to OVA, and Group 3=lowantibody response to OVA and Population mean =PM. Significantdifferences between groups are indicated with lower case letters anddifferences over time are indicated by different upper case letters(P<0.05).

[0671]FIG. 10. Percent increase in skin thickness 48 hours afterchallenge with the purified protein derivative of tuberculin (PPD) incows and heifers previously sensitized to BCG.

[0672]FIG. 11. Type III LS Means of lymphocyte counts (cells/mL) inblood during the peripartum period. Group 1=high antibody response toOVA, Group 2=average antibody response to OVA, and Group 3=low antibodyresponse to OVA and Population mean=PM. Significant differences betweengroups are indicated with lower case letters and differences over timeare indicated by different upper case letters (P<0.05).

[0673]FIG. 12. Type III LS Means of projected 305 day yield for milk(1A), protein (1B), and fat (1C). Group 1=high antibody response, Group2=average antibody response, and Group 3=low antibody response based ondescribed index, and Population mean (PM). Significant differencesbetween groups are indicated with lower case letters (P<0.05).

[0674]FIG. 13. LS Means of antibody response to OVA in A) serum and B)whey by antibody response group following immunization at weeks −8, −3,and 0 as measured by enzyme linked immunosorbent assay (ELISA). Group1=high measurable response; Group 2=lack of measurable response toimmunization postpartum (week 0); Group 3=lack of measurable response toimmunization pre- and postpartum; Pop=population mean. Animalclassification is based on serum antibody response to OVA. Significantdifferences between animals in the three groups are indicated bydifferent letters above error bars (P≦0.05).

[0675]FIG. 14. LS Means of hormone concentrations by antibody responsegroup as determined by radioimmunoassay (RIA). FIG. 14A=growth hormone(GH); FIG. 14B=insulin-like growth factor-I (IGF-I); FIG. 14C=Cortisol.Group 1=high measurable response in serum; Group 2=lack of measurableresponse to immunization postpartum (week 0); Group 3=lack of measurableresponse to immunization pre- and postpartum; Pop=population mean.Animal classification is based on serum antibody response to ovalbumin(OVA). Nat. log.=natural logarithm. Significant differences betweenanimals in the three groups are indicated by different letters abovestandard error bars (P≦0.05).

[0676]FIG. 15. Percent disease occurrence by antibody response group.Group 1=high measurable response in serum; Group 2=lack of measurableresponse to immunization postpartum (week 0); Group 3=lack of measurableresponse to immunization pre- and postpartum. Animal classification isbased on serum antibody response to ovalbumin (OVA). TABLE 1 Analysis ofvariance of antibody response to ovalbumin (OVA) and E. coli J5, and theconcentration of immunoglobulin G_(1&2) in serum and whey Source ofVariation Dependent R^(2a) C.V.^(b) Cow Group* Variable (%) (%)(Group)^(c) Week Group^(d) Week Antibody Response Serum anti- 88.3121.66 0.0001 0.0001 0.0001 0.0001 OVA Whey anti- 74.97 −127.86 0.00010.0001 0.0001 0.09 OVA Serum anti- 78.29 −60.42 0.0001 0.0001 0.0001ns^(f) E. coli Immunoglo- bulin concentration Serum IgG₁ 64.91 6.97 ns0.0001 ns ns Serum IgG₂ 67.19 4.22 ns 0.0001 0.0001 0.004 Whey IgG₁87.16 18.92 ns 0.0001 ns ns Whey IgG₂ 95.11 14.15 ns 0.0001 ns ns

[0677] TABLE 2 Analysis of variance of antibody response to ovalbumin(OVA) and E. coli J5, the concentration of immunoglobulin F_(1&2) inserum and whey, and somatic cell score (SCS) Source of VariationDependent R^(2a) C.V.^(b) Season- Group* Group* Parity* Variable (%) (%)Herd yr^(c) Cow^(d) Group^(e) Parity parity Week Week Week AntibodyResponse Serum anti-OVA 79.41 27.63 — — 0.0001 0.0001 0.096 ns^(f)0.0001 0.0001 — Whey anti-OVA 73.73 32.16 0.02 — 0.0001 0.0001 ns ns0.0001 ns — Herd 1 75.34 — — — 0.0001 0.0001 0.0001 — 0.0001 0.05 —130.01^(h) Herd 2 71.29 −524.16 — — 0.0001 0.007 — — 0.0001 0.07 — Herd3 82.72 6682.1 — — 0.0001 0.0002 — — 0.0001 ns — Serum anti-E. coli74.23 −43.72 0.003 — 0.0001 — 0.0004 — 0.0001 — 0.0001 Herd 1 78.63−54.79 — — 0.0001 ns — — 0.0001 0.06 — Herd 2 76.89 −45.16 — — 0.0001 ns0.0001 0.0001 0.0001 ns — Herd 3 70.63 −31.53 — — 0.0001 ns — — 0.00010.002 — Immunoglobulin Concentration Serum IgG₁ 49.74 7.53 — — ns 0.07ns ns 0.0001 ns — Serum IgG₂ 63.14 4.34 0.0001 — 0.0001 ns 0.04 ns 0.025ns — Herd 1 59.97 4.67 — — 0.02 ns — — 0.0015 ns — Herd 2 48.49 4.36 — —0.021 0.08 ns 0.08 ns ns — Herd 3 56.14 3.7 — — 0.005 — 0.04 — 0.09 — nsWhey IgG₁ 90.1 15.11 — — ns ns 0.01 ns 0.0001 ns — Whey IgG₂ 96.85 13.50.03 — ns ns 0.0009 ns 0.0001 ns — Herd 1 94.85 14.96 — — ns ns — —0.0009 ns — Herd 2 ns ns — — ns ns 0.08 ns 0.02 ns — Herd 3 97.41 12.94— — ns 0.097 — — 0.0001 ns — Somatic Cell Score SCS (Herd 1) 83.51 44.89— — 0.0001 ns — — 0.0013 ns — SCS (Herd 2) 81.43 46.7 — — 0.0001 ns — —0.0001 ns — SCS (Herd 3) 78.84 26.54 — — 0.0001 ns — — ns ns —

[0678] TABLE 3 Percent Occurrence (%) of clinical mastitis by antibodyresponse group within herd % Occurrence of Mastitis within an AntibodyResponse Group Overall Mastitis Frequency Herd Group 1 Group 2 Group 3by Herd Herd 1 # of animals n = 4  n = 22 n = 6  n = 32 % with mastitis0 21.7 33.3 21.2 Herd 2 # of animals n = 13 n = 47 n = 7  n = 67 % withmastitis 15.4 2.1 0 4.5 Herd 3 # of animals n = 1  n = 26 n = 10 n = 37% with mastitis 0 11.5 10 10.8 All herds # of animals n = 18 n = 95 n =23  n = 136 % Overall Mastitis 11.1 9.3 13.6 — Frequency by Group

[0679] TABLE 4 Analysis of Variance of lymphocyte proliferation toovalbumin (OVA) and concanavalin A (Con A), lymphocyte and neutrophilnumber, delayed type hypersensitivity and somatic cell score DependentR2^(a) Season- Group Group Parity* Variable (%) CV^(b) Herd Year^(c)Cow^(d) Group^(e) Parity *Parity Week *Week Week LymphocyteProliferation Unstimulated 58.69 11.5 —^(f) — 0.0001 ns^(g) ns 0.00010.0001 0.05 — OVA 85.75 6.34 — — 0.0001 0.01 0.0006 0.0001 ns 0.009 —Con A 67.2 5.15 — — 0.0001 ns 0.004 0.0001 0.0001 0.0002 — CompleteBlood Cell Counts Lymphocytes 83.16 17.31 — — 0.0001 ns 0.0001 0.00010.08 ns — Segmented 37.42 2.81 — — 0.003 ns ns ns ns ns — NeutrophilsBanded ns ns — — ns ns ns ns ns ns — Neutrophils Somatic Cell Score Herd1 83.5 44.89 — — 0.0001 ns — — 0.001 ns — Herd 2 81.43 46.7 — — 0.0001ns — — 0.0001 ns — Herd 3 78.84 26.54 — — 0.0001 ns — — ns ns —

[0680] TABLE 5 Correlation analysis of antibody to ovalbumin (OVA) withunstimulated and stimulated lymphocyte proliferation to OVA andconcanavalin A (Con A), and cutaneous delayed type hypersensitivity(DTH) response to purified protein derivative (PPD) of M. tuberculosis.Dependent Variable Independent Variable r² P-value Unstimulated Antibodyto OVA −0.26 0.0001 Lymphocyte Proliferation OVA-Stimulated Antibody toOVA −0.27 0.0001 Lymphocyte Proliferation Con A-Stimulated Antibody toOVA −0.14 0.0001 Proliferation DTH - 48 hours Antibody to OVA ns nsDTH - 72 hours Antibody to OVA ns ns

[0681] TABLE 6 Analysis of Variance (ANO VA) of projected 305-day milk,protein and fat yields Dependent R^(2a) Season- Group Variable (%)CV^(b) Herd Year^(c) Group^(d) Parity *Parity Milk yield 19.5 14.98 —^(e) — 0.06 0.0001 0.0001 Protein yield 15.26 14.76 — — 0.0001 0.00010.0001 Fat Yield 17.51 13.53 — — 0.0001 0.0001 0.0001

[0682] TABLE 7 Analysis of variance of antibody response to ovalbumin(OVA) and E. coli J5, and the concentration of immunoglobulin G_(1&2) inserum and whey Source of Variation Dependent R^(2a) C.V.^(b) Cow Group*GH* IGF-I* Cort* Variable (%) (%) (Group)^(c) Week Group^(d) Week GH^(e)Week IGF-I^(f) Week Cort^(g) week Antibody Response Serum OVA 94.1914.01 0.0001 ns^(h) 0.005 0.0001 0.15 ns ns ns ns ns Whey OVA 83.8137.36 0.006 0.06 0.003 ns ns ns 0.12 0.0005 ns ns E. coli 79.1 97.180.0002 ns ns ns ns ns ns ns ns ns Immunoglobulin Serum IgG₁ 73.17 7.88ns ns ns ns ns ns ns ns ns ns Serum IgG₂ 75.47 4.63 ns ns 0.001 ns ns nsns ns ns ns Whey IgG₁ 94.66 16.46 ns ns ns ns ns ns ns 0.07 ns ns WheyIgG₂ 87.25 30.92 ns — ns — n — ns — ns —

[0683] TABLE 8 Correlation analysis of hormone concentration withantibody response to ovalbumin (OVA), and E. coli J5, and theconcentration of IgG_(1&2) in serum and whey Dependent VariableIndependent Variable r^(2a) P value Antibody Response Serum OVA GH^(b)0.29 0.001 IGF-I^(c) −0.19 0.04 Cortisol^(d) 0.17 0.06 Whey OVA GH 0.310.0005 IGF-I −0.22 0.01 Cortisol — ns E. coli J5 GH 0.18 0.04 IGF-I — nsCortisol — ns Radial Immunodiffusion Serum IgG₁ GH −0.26 0.01 IGF-I 0.190.07 Cortisol — ns Serum IgG₂ GH — ns IGF-I — ns Cortisol — ns Whey IgG₁GH 0.26 0.03 IGF-I −0.2 0.1 Cortisol — ns Whey IgG₂ GH — ns IGF-I — nsCortisol — ns

We claim:
 1. A method of ranking the immune response of a test animalwithin a population of animals under stress comprising: (a) immunizingthe animals with at least one antigen at least once before the onset ofthe stress; and (b) measuring the antibody response of the animals tothe at least one antigen at least once before the onset of the stressand at least once during the stress, wherein a change in antibodyresponse from before the onset of stress to during the stress for thetest animal that is greater than the average change in antibody responsefrom before the onset of the stress to during the stress for thepopulation indicates that the animal is a high immune responder.
 2. Amethod of ranking the immune response of a test animal within apopulation of animals under stress comprising: (a) immunizing theanimals with at least one antigen at least once before the onset of thestress and at least once during the stress; and (b) measuring theantibody response of the animals to the at least one antigen at leastonce before the onset of the stress and at least once during the stress,wherein a change in antibody response from before the onset of stress toduring the stress for the test animal that is greater than the averagechange in antibody response from before the onset of the stress toduring the stress for the population indicates that the animal is a highimmune responder.
 3. A method of ranking the immune response of a testanimal within a population of animals under stress comprising: (a)immunizing the animals with at least one antigen at least once beforethe onset of the stress and at least once during the stress; and (b)measuring the antibody response of the animals to the at least oneantigen at least once before the onset of the stress and at least twiceduring the stress, wherein the changes in antibody responses betweeneach measurement are added to provide a total antibody response and atotal antibody response for the test animal that is greater than anaverage total antibody response for the population indicates that theanimal is a high immune responder.
 4. The method according to claim 3,wherein negative changes in antibody responses during the stress aremultiplied with a co-efficient greater than
 1. 5. The method accordingto claim 4, wherein negative changes in antibody responses during thestress are multiplied with a co-efficient of about 1.5.
 6. The methodaccording to claim 3, wherein the stres s is selected from the groupconsisting of disease, weaning, castration, dehorning, branding,shipping, change in ration, social disruption, restraint,periparturition and exercise.
 7. The method according to claim 6,wherein the stress is periparturition.
 8. The method according to claim1, wherein the animal is bovine.
 9. The method according to claim 8,wherein the bovine is selected from a multiparous cow and a primiparouscow.
 10. The method according to claim 8, wherein the bovine is amultiparous cow.
 11. The method according to claim 1, wherein theantigen is selected from the group consisting of hen egg white lysozyme,human serum albumin, tyrosine-glycine-alanine-lysine copolymer andovalbumin.
 12. The method according to claim 11, wherein the antigen isovalbumin.
 13. The method according to claim 12, wherein the antigen isformualted with an adjuvant selected from the group consisting ofFreunds complete adjuvant (FCA), non-ulcerative Freunds adjuvant (NUFA),complete NUFA and mycobacteria cell wall extract.
 14. The methodaccording to claim 1, wherein the antigen is formulated into a vaccine.15. The method according to claim 14, wherein the vaccine is Escherichiacoli J5.
 16. The method according to claim 1, wherein a source formeasuring the antibody response is selected from the group consisting ofblood and milk.
 17. The method according to claim 7, wherein themeasuring of the antibody response at least once before the onset of thestress is at about 8 weeks before parturition and the measuring of theantibody response at least once during the stress is at about 3 weeksbefore parturition and at about parturition.
 18. The method according toclaim 7, wherein the measuring of the antibody response at least oncebefore the onset of the stress is at about 8 weeks before parturitionand the measuring of the antibody response at least once during thestress is at about 3 weeks before parturition, at about parturition andat about 3 weeks after parturition.
 19. The method according to claim 7,wherein the immunizing the animals at least once before the onset of thestress is at about 8 weeks before parturition and the immunizing theanimals at least once during the stress is at about 3 weeks beforeparturition and at about parturition.
 20. The method according to claim7, wherein the immunizing the animals at least once before the onset ofthe stress is at about 8 weeks before parturition and the immunizing theanimals at least once during the stress is at about 3 weeks beforeparturition, at about parturition and at about 3 weeks afterparturition.
 21. A method of ranking the immune response of a testanimal within a population of animals under stress comprising: (a)immunizing the animals with at least one antigen at least once beforethe onset of the stress; (b) measuring the antibody response of theanimals to the at least one antigen at least once before the onset ofthe stress and at least once during the stress; and (c) calculating amathematical index of the antibody response, wherein the mathematicalindex is: y=primary antibody response, wherein (i) y is the immuneresponse; and (ii) the primary response is the difference in antibodyquantity at a first time point before the onset of stress and a secondtime point during the stress, wherein the animal is immunized at thefirst time point before the onset of stress; wherein a test animalhaving a y value greater than about one standard deviation above theaverage of the y value for the population is a high immune responder.22. A method of ranking the immune response of a test animal within apopulation of animals under stress comprising: (a) immunizing theanimals with at least one antigen at least once before the onset of thestress and at least once during the stress; (b) measuring the antibodyresponse of the animals to the at least one antigen at least once beforethe onset of the stress and at least two times during the stress; and(c) calculating a mathematical index of the antibody response, whereinthe mathematical index is: y=primary antibody response+secondaryantibody response, wherein (i) y is the immune response; (ii) theprimary response is the difference in antibody quantity at a first timepoint before the onset of stress and a second time point during thestress, wherein the animal is immunized at the first time point beforethe onset of stress; and (iii) the secondary response is the differencein antibody quantity at a second time point during the stress and at athird time point during the stress, wherein the animal is immunized atthe second time point during the stress; wherein with animals exhibitinga negative secondary response, the secondary response is weighted with aco-efficient greater than 1, and a test animal having a y value greaterthan about one standard deviation above the average of the y value forthe population is a high immune responder.
 23. A method of ranking theimmune response of a test animal within a population of animals understress comprising: (a) immunizing the animals with at least one antigenat least once before the onset of the stress and at least twice duringthe stress; (b) measuring the antibody response of the animals to the atleast one antigen at least once before the onset of the stress and atleast three times during the stress; and (c) calculating a mathematicalindex of the antibody response, wherein the mathematical index is:y=primary antibody response+secondary antibody response+tertiaryantibody response, wherein (i) y is the immune response; (ii) theprimary response is the difference in antibody quantity at a first timepoint before the onset of stress and a second time point during thestress, wherein the animal is immunized at the first time point beforethe onset of stress; (iii) the secondary response is the difference inantibody quantity at a second time point during the stress and at athird time point during the stress, wherein the animal is immunized atthe second time point during the stress; and (iv) the tertiary responseis the difference in antibody quantity at a third time point during thestress and at a fourth time point during the stress, wherein the animalis immunized at the third time point during the stress; wherein withanimals exhibiting negative secondary and/or tertiary antibodyresponses, the secondary and/or tertiary antibody responses are weightedwith a co-efficient greater than 1, and a test animal having a y valuegreater than about one standard deviation above the average of the yvalue for the population is a high immune responder.
 24. A method ofranking the immune response of a test animal within a population ofanimals under stress comprising: (a) immunizing the animals with atleast one antigen at least once before the onset of the stress and atleast twice during the stress; (b) measuring the antibody response ofthe animals to the at least one antigen at least once before the onsetof the stress and at least four times during the stress; and (c)calculating a mathematical index of the antibody response, wherein themathematical index is: y=primary antibody response+secondary antibodyresponse+tertiary antibody response+quaternary antibody response,wherein (i) y is the immune response; (ii) the primary response is thedifference in antibody quantity at a first time point before the onsetof stress and a second time point during the stress, wherein the animalis immunized at the first time point before the onset of stress; (iii)the secondary response is the difference in antibody quantity at asecond time point during the stress and at a third time point during thestress, wherein the animal is immunized at the second time point duringthe stress; (iv) the tertiary response is the difference in antibodyquantity at a third time point during the stress and at a fourth timepoint during the stress, wherein the animal is immunized at the thirdtime point during the stress; and (v) the quaternary response is thedifference in antibody quantity at a fourth time point during the stressand at a fifth time point after the stress; wherein with animalsexhibiting negative secondary and/or tertiary antibody responses thesecondary and/or tertiary antibody responses are weighted with aco-efficient greater than 1, and a test animal having a y value greaterthan about one standard deviation above the average of the y value forthe population is a high immune responder.
 25. A method of ranking theimmune response of a test animal within a population of animals understress comprising: (a) immunizing the animals with at least one antigenat least once before the onset of the stress; (b) measuring the antibodyresponse of the animals to the at least one antigen at least once beforethe onset of the stress and at least once during the stress; (c)exposing the animals to an antigen which can evoke a cell-mediatedimmune response (CMIR); and (d) measuring at least one indicator of theCMIR in the animals during the stress, wherein the changes in antibodyresponses between each measurement are added to provide a total antibodyresponse and the measurement of the indicator is combined with the totalantibody response to provide an immune response and a test animal havingan immune response that is greater than an average immune response forthe population indicates that the animal is a high immune responder. 26.The method according to claim 25, wherein in (a) the animals areimmunized with at least one antigen before the onset of stress and atleast once during stress.
 27. The method according to claim 25, whereinthe indicator is selected from the group consisting of cytokines,delayed-type hypersensitivity and in vitro lymphocyte proliferation toat least one antigen.
 28. The method according to claim 27, wherein theindicator is delayed-type hypersensitivity.
 29. The method according toclaim 28, wherein the antigen which can invoke a CMIR is selected fromthe group consisting of an intracellular organism and a mitogen.
 30. Themethod according to claim 29, wherein the intracellular organism isselected from the group consisting of Mycobacterium bovis andMycobacterium phlei.
 31. The method according to claim 29, wherein themitogen is selected from the group consisting of concanavalin A andphytohaemaglutinin.
 32. The method according to claim 29, wherein theantigen further comprises an adjuvant selected from the group consistingof Freunds complete adjuvant (FCA), non-ulcerative Freunds adjuvant(NUFA), complete NUFA and mycobacteria cell wall extract.
 33. A methodof ranking the immune response of a test animal within a population ofanimals under stress comprising: (a) immunizing the animals with atleast one antigen at least once before the onset of the stress; (b)measuring the antibody response of the animals to the at least oneantigen at least once before the onset of the stress and at least onceduring the stress; (c) exposing the animals to an antigen which canevoke a cell-mediated immune response (CMIR); (d) measuring at least oneindicator of the CMIR in the animals during the stress; and (e)calculating a mathematical index of the antibody response and CMIR,wherein the mathematical index is: y=primary antibody response+CMIR,wherein (i) y is the immune response; (ii) the primary response is thedifference in antibody quantity at a first time point before the onsetof stress and a second time point during the stress, wherein the animalis immunized at the first time point before the onset of stress; (iii)CMIR is the measurement obtained from at least one method of determiningCMIR, wherein a test animal having a y value greater than about onestandard deviation above the average of the y value for the populationis a high immune responder.
 34. A method of ranking the immune responseof a test animal within a population of animals under stress comprising:(a) immunizing the animals with at least one antigen at least oncebefore the onset of the stress and at least once during the stress; (b)measuring the antibody response of the animals to the at least oneantigen at least once before the onset of the stress and at least twotimes during the stress; (c) exposing the animals to an antigen whichcan evoke a cell-mediated immune response (CMIR); (d) measuring at leastone indicator of the CMIR in the animals during the stress; and (e)calculating a mathematical index of the antibody response and CMIR,wherein the mathematical index is: y=primary antibody response+secondaryantibody response+CMIR, wherein (i) y is the immune response; (ii) theprimary response is the difference in antibody quantity at a first timepoint before the onset of stress and a second time point during thestress, wherein the animal is immunized at the first time point beforethe onset of stress; (iii) the secondary response is the difference inantibody quantity at a second time point during the stress and at athird time point during the stress, wherein the animal is immunized atthe second time point during the stress;and (iv) CMIR is the measurementobtained from at least one method of determining CMIR, wherein withanimals exhibiting a negative secondary response, the secondary responseis weighted with a co-efficient greater than 1, and a test animal havinga y value greater than about one standard deviation above the average ofthe y value for the population is a high immune responder.
 35. A methodof ranking the immune response of a test animal within a population ofanimals under stress comprising: (a) immunizing the animals with atleast one antigen at least once before the onset of the stress and atleast twice during the stress; (b) measuring the antibody response ofthe animals to the at least one antigen at least once before the onsetof the stress and at least three times during the stress; (c) exposingthe animals to an antigen which can evoke a cell-mediated immuneresponse (CMIR); (d) measuring at least one indicator of the CMIR in theanimals during the stress; and (e) calculating a mathematical index ofthe antibody response and CMIR, wherein the mathematical index is:y=primary antibody response+secondary antibody response+tertiaryantibody response+CMIR, wherein (i) y is the immune response; (ii) theprimary response is the difference in antibody quantity at a first timepoint before the onset of stress and a second time point during thestress, wherein the animal is immunized at the first time point beforethe onset of stress; (iii) the secondary response is the difference inantibody quantity at a second time point during the stress and at athird time point during the stress, wherein the animal is immunized atthe second time point during the stress; (iv) the tertiary response isthe difference in antibody quantity at a third time point during thestress and at a fourth time point during the stress, wherein the animalis immunized at the third time point during the stress; and (v) CMIR isthe measurement obtained from at least one method of determining CMIR,wherein with animals exhibiting negative secondary and/or tertiaryantibody responses, the secondary and/or tertiary antibody responses areweighted with a co-efficient greater than 1, and a test animal having ay value greater than about one standard deviation above the average ofthe y value for the population is a high immune responder.
 36. A methodof ranking the immune response of a test animal within a population ofanimals under stress comprising: (a) immunizing the animals with atleast one antigen at least once before the onset of the stress and atleast twice during the stress; (b) measuring the antibody response ofthe animals to the at least one antigen at least once before the onsetof the stress and at least four times during the stress; (c) exposingthe animals to an antigen which can evoke a cell-mediated immuneresponse (CMIR); (d) measuring at least one indicator of the CMIR in theanimals during the stress; and (e) calculating a mathematical index ofthe antibody response and CMIR, wherein the mathematical index is:y=primary antibody response+secondary antibody response+tertiaryantibody response+quaternary antibody response+CMIR, wherein (i) y isthe immune response; (ii) the primary response is the difference inantibody quantity at a first time point before the onset of stress and asecond time point during the stress, wherein the animal is immunized atthe first time point before the onset of stress; (iii) the secondaryresponse is the difference in antibody quantity at a second time pointduring the stress and at a third time point during the stress, whereinthe animal is immunized at the second time point during the stress; (iv)the tertiary response is the difference in antibody quantity at a thirdtime point during the stress and at a fourth time point during thestress, wherein the animal is immunized at the third time point duringthe stress; (v) the quaternary response is the difference in antibodyquantity at a fourth time point during the stress and at a fifth timepoint after the stress; and (vi) CMIR is the measurement obtained fromat least one method of determining CMIR, wherein with animals exhibitingnegative secondary and/or tertiary antibody responses, the secondaryand/or tertiary antibody responses are weighted with a co-efficientgreater than 1, and a test animal having a y value greater than aboutone standard deviation above the average of the y value for thepopulation is a high immune responder.
 37. The method according to claim25, wherein the stress is parturition.
 38. The method according to claim36, wherein the stress is parturition and the first time point beforethe onset of stress is at about 8 weeks before parturition, the secondtime point during the stress is at about 3 weeks before parturition, thethird time point during stress is parturition, and the fourth time pointduring stress is at about 3 weeks after parturition.